ijee_february_2013_extension (vol 01-no 01) issue

February 2014 Volume 07 No 01 ISSN 0974-5904

INTERNATIONAL JOURNAL OF EARTH SCIENCES AND ENGINEERING

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IIT-Bombay, Powai, INDIA

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Research, SAFAT, KUWAIT

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Mani Ram Saharan National Geotechnical Facility

DST, Dehradun, INDIA

Abdullah Saand Quaid-e-Awam University of Eng.

Sc. & Tech., Sindh, PAKISTAN

Subhasish Das IIT- Kharagpur, Kharagpur

West Bengal, INDIA

S Viswanathan IIT- Bombay, Powai, Mumbai

Maharashtra, INDIA

Katta Venkataramana NITK- Surathkal

Karnataka, INDIA

Ramana G V IIT– Delhi, Hauz Khas

New Delhi, INDIA

Usha Natesan Centre for Water Resources

Anna University, Chennai, INDIA

K U Maheshwar Rao IIT- Kharagpur, Kharagpur

West Bengal, INDIA

Kalachand Sain National Geophysical Research Institute,

Hyderabad, INDIA

G S Dwarakish NITK- Surathkal

Karnataka, INDIA

M K Nagaraj NITK- Surathkal

Karnataka, INDIA

R Sundaravadivelu IIT- Madras

Tamil Nadu, INDIA

S M Ramasamy Gandhigram Rural University

Tamil Nadu, INDIA

M R Madhav JNTU- Kukatpally, Hyderabad

Andhra Pradesh, INDIA

Chachadi A G Goa University, Taleigao Plateau

Goa, INDIA

R Bhima Rao IMMT, Bhubaneswar

Odissa, INDIA

Gholamreza Ghodrati Amiri Iran University of Sci. & Tech.

Narmak, Tehran, IRAN

C Natarajan NIT- Tiruchirapalli,

Tamil Nadu, INDIA

N Ganesan NIT- Calicut, Kerala

Kerala, INDIA

Shamsher B. Singh BITS- Pilani, Rajasthan

Rajasthan, INDIA

Pradeep Kumar R IIIT- Gachibowli, Hyderabad


Vladimir e Vigdergauz ICEMR RAS, Moscow

RUSSIA

D P Tripathy National Institute of Technology

Rourkela, INDIA

E Saibaba Reddy JNTU- Kukatpally, Hyderabad


Chowdhury Quamruzzaman Dhaka University

Dhaka, BANGLADESH

Parekh Anant kumar B Indian Institute of Tropical

Meteorology, Pune, INDIA

Datta Shivane Central Ground Water Board

Hyderabad, INDIA

Gopal Krishan National Institute of Hydrology

Roorkee, INDIA

Karra Ram Chandar NITK- Surathkal

Karnataka, INDIA

Prasoon Kumar Singh Indian School of Mines, Dhanbad

Jharkhand, INDIA

A G S Reddy Central Ground Water Board,

Pune, Maharashtra, INDIA

Rajendra Kumar Dubey Indian School of Mines, Dhanbad

Jharkhand, INDIA

Subhasis Sen Retired Scientist

CSIR-Nagpur, INDIA

M V Ramanamurthy Geological Survey of India

Bangalore, INDIA

A Nallapa Reddy

Chief Geologist (Retd.)

ONGC Ltd., INDIA

Bijay Singh Ranchi University, Ranchi

Jharkhand, INDIA

B R Raghavan Mangalore University, Mangalore

Karnataka, INDIA

C Sivapragasam Kalasalingam University,

Tamil Nadu, INDIA

Xiang Lian Zhou ShangHai JiaoTong University

ShangHai, CHINA

K. Bheemalingeswara Mekelle University

Mekelle, ETHIOPIA

Kripamoy Sarkar Assam University

Silchar, INDIA

Anand V. Shivapur SDM College of Engg. and Tech.

Karnataka, INDIA

S Suresh Babu Adhiyamaan college of Engineering

Tamil Nadu, INDIA

Nandipati Subba Rao Andhra University, Visakhapatnam


M Suresh Gandhi University of Madras,

Tamil Nadu, INDIA

Debadatta Swain National Remote Sensing Centre

Hyderabad, INDIA

H K Sahoo Utkal University, Bhubaneswar

Odissa, INDIA

R N Tiwari Govt. P G Science College, Rewa

Madhya Pradesh, INDIA

B M Ravindra Dept. of Mines & Geology, Govt. of

Karnataka, Mangalore, INDIA

M V Ramana CSIR NIO

Goa, INDIA

N Rajeshwara Rao University of Madras

Tamil Nadu, INDIA

R Baskaran Tamil University, Thanjavur

Tamil Nadu, INDIA

Salih Muhammad Awadh College of Science

University of Baghdad, IRAQ

Sonali Pati Eastern Academy of Science and

Technology, Bhubaneswar, INDIA

Nuh Bilgin

Istanbul Technical University

Maslak, ISTANBUL

Naveed Ahmad University of Engg. & Technology,

Peshawar, PAKISTAN

Raj Reddy Kallu University of Nevada

1665 N Virginia St, RENO

Manish Kumar Tezpur University

Sonitpur, Assam, INDIA

Raju Sarkar

Delhi Technological University

Delhi, INDIA

Jaya Kumar Seelam National Institute of Oceanography Dona

Paula, Goa, INDIA

Safdar Ali Shirazi University of the Punjab,

Quaid-i-Azam Campus, PAKISTAN

C N V Satyanarayana Reddy Andhra University

Visakhapatnam, INDIA

S M Hussain University of Madras

Tamil Nadu, INDIA

Glenn T Thong Nagaland University

Meriema, Kohima, INDIA

T J Renuka Prasad Bangalore University

Karnataka, INDIA

Deva Pratap National Institute of Technology

Warangal, INDIA

Samir Kumar Bera Birbal sahni institute of palaeobotany,

Lucknow, INDIA

Mohammed Sharif Jamia University

New Delhi, INDIA

A M Vasumathi K.L.N. College of Inf. Tech.

Pottapalayam, Tamil Nadu, INDIA

Vladimir Vigdergauz ICEMR, Russian Academy of Sciences

Moscow, RUSSIA

C J Kumanan Bharathidasan University

Tamil Nadu, INDIA

B R Manjunatha Mangalore University

Karnataka, INDIA

Ranjith Pathegama Gamage Monash University, Clayton

AUSTRALIA

Ch. S. N. Murthy NITK- Surathkal

Karnataka, INDIA

K. Subramanian Coimbatore Institute of Technology

Tamil Nadu, INDIA

INDEX

Volume 07 February 2014 No.01

EDITORIAL NOTE

River Linking - Indian Scenario

By P R REDDY and D VENKAT REDDY

RESEARCH PAPERS

Geochemical Investigations on Thermal and Cold Springs at Dumka District,

Jharkhand, India

By HEMANT K SINGH, D CHANDRASEKHARAM, TRUPTI G and B SINGH

190-194

Prediction of Daily Pan Evaporation Using Support Vector Machines

By LEELADHAR PAMMAR and PARESH CHANDRA DEKA

195-202

Fluoride Distribution in the Groundwater of Narsampet Area, Warangal District,

Andhra Pradesh, India

By V SUDARSHAN, S GEETA, A NARSIMHA, S SHANKAR and A RAVI KUMAR

203-212

Ore Microscopic Study of the Gold Mineralization within Chandil Formation, North

Singhbhum Mobile Belt, Eastern India

By KARUN KUMAR CHANDAN, VANDANA JHA, SUBRATA ROY, MOUSOMA

KHATUN, PRABODHA R. SAHOO and SAHENDRA SINGH

213-222

Applications of Expert Systems in Mining Industry: A Review

By K RAM CHANDAR and H AGARWAL

223-229

Interpretation of Depositional Environment of Miocene Sequence Using Electrofacies

Analysis in the Well Bakhrabad # 09, Bengal Basin

By ABU REZA MD. TOWFIQUL ISLAM, MD. AMINUL ISLAM, MD. EMDADUL HAQUE

and KHURSHIDA JAHAN

230-239

Structural Analyses of Lesser Himalayan Sequence and Strain Calculation of the

Shergaon Conglomerate of West Kameng District of Arunachal Pradesh, India

By NANDITA MAZUMDAR, SANTANU BHATTACHARJEE, SANDIP NANDY and

K P SARMA

239-250

A Geo-Technical assessment of Slope stability condition at Lovedale Club slide,

Lovedale, The Nilgiris, Tamil Nadu, India

By E SARANATHAN, SUGANYA KANAGASABAI, M KANNAN and G K VENKATRAMAN

251-259

Land-Slide Hazards of October 2009 at Karwar, Karnataka: A Lesson for Planning

Developmental Activities in the Tropical Ghat Regions

By V S HEGDE, KRISHNAPRASAD P A, SHALINI R, DEEPMALA NILAMWAR,

TEJASWINI B, GIRISH K H and C S MALEWADI

260-268

Sedimentary Basin Screening Techniques using Remote Sensing Bathymetry Data and

ArcGIS for Eastern Continental Margin of India

By MRUTYUNJAYA PANIGRAHI and MADHUMITA DAS

269-274

Geospatial assessment of Coral and Mangrove Environs of the Andaman Islands

By MAHENDRA R S, MOHANTY P C, BISOYI H and SRINIVASA KUMAR

275-279

Integrating Fuzziness to Wildlife Relocation and Habitat Analysis in Rajasthan, India

By SUMAN SINHA

280-288

Benthic Foraminifera in a Sedimentary Core from Kollam Coastal Plain, South

Kerala, India

By R GAYATHRI, R NAGENDRA, A N REDDY, P SATHIYAMOORTHY and N SURESH

289-296

Solid Waste Transportation Cost Using Arm Roll in Malang City, Indonesia

By BURHAMTORO, ACHMAD WICAKSONO, M BISRI and SOEMARNO

297-304

Structural Health Monitoring Techniques in Civil Engineering: An Overview

By BHAVANA PATEL S S, KATTA VENKATARAMANA, K S BABU NARAYAN,

BHAGYASHRI PARLA and YUKINOBU KIMURA

305-312

Dynamic characteristics of a cable-stayed pedestrian and cyclists footbridge 120 m

long

By IZABELA J MURZYN

313-319

Experimental studies on the effects of corrosion on the flexural strength of RC beams

By POORNACHANDRA PANDIT, KATTA VENKATARAMANA, K S BABUNARAYAN,

BHAGYASHRI PARLA and YUKINOBU KIMURA

320-324

Modelling of the Cu and Fe transport in sand-bentonite and sand-fly ash mixtures

By SHANKARA, MAYA NAIK and P V SIVAPULLAIAH

325-330

Non Destructive Tests with Rebound Hammer and Ultrasonic Pulse Velocity

Measurements on Geopolymer Concrete

By SHANKAR H SANN and R B KHADIRANAIKAR

331-335

Performance Studies on Cement Stabilized Gravel Soils Exposed to Acid Environment

By A C S V PRASAD and C N V SATYANARAYANA REDDY

336-340

Structural Characteristics of Laterite Blocks

By GANESHA MOGAVEERA and G SARANGAPANI

341-348

An Experimental Investigation on Some Strength Properties of Light Weight Blended

Aggregate Concrete

By V BHASKAR DESAI, A SATHYAM and K MALLIKARJUNAPPA

349-355

Estimation of Methane from Flooded Paddy fields in Andhra Pradesh

By ANUP MATTHEW, ATUL V RAO and VENKATA RAVIBABU MANDLA

356-362

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ISSN 0974-5904, Volume 06, No. 06

February 2014, Editorial Note

Editorial Note


P R REDDY1 AND D VENKAT REDDY

2

1CSIR – National Geophysical Research Institute (NGRI), Hyderabad-500 007, Andhra Pradesh, INDIA

2Dept of Civil Engg, NITK, Surathkal, Srinivasnagar-575025, Mangalore, D.K, Karnataka, INDIA

Email: [email protected], [email protected]

Introduction

With uncontrolled increase of population, water related

problems are introducing number of hurdles for over all

development of our agriculture based economy. The per

capita availability of water (PCA) in India is only 2200

m3/year as against 17500 m3/year in Russia. As per

international standards, a country with less than 1700 cu

m of PCA is considered water-stressed, when the PCA

drops to 1000 m3, it is said to be water-scarce.

Demographic projections indicate that by the year 2050,

the country’s population would be stabilized at around

1640 million; at that time, the PCA would be

precariously placed at 1100 m3; but the situation, it is

feared, may escalate to a higher figure (MOWR 1999).

If the population increases further, which is likely, the

PCA would sink to less than 1000 m3. It does not

constitute even 10% of the corresponding value in the

developed countries. The current usage of 600 BCM of

water by the country has to be increased to 1200 BCM

by 2050 to keep abreast of the needs of the increasing

population. Many learned, some others with vested

interests and rest due to ignorance have come out with

various options to address these problems; including

river linking. River Linking comes under a project

linking two or more rivers by creating a network of

manually created canals, and providing needed water to

land areas that otherwise do not have river water access

and reducing the flow of water to sea using this means.

It is based on the assumptions that surplus water in

some rivers can be diverted to Deficit Rivers by creating

a network of canals to interconnect the rivers. It is

noticed that such a linking on paper looks very simple

and viable. But, in reality a systematic planning is

essential, taking in to consideration socio-economic

factors, area specific temporal and spatial variations and

environmental aspects. River Linking, in a mega scale,

is not practiced internationally due to various

limitations. However, some specific case studies

covering different parts of Europe, USA, Africa and

South East Asia indicate the limitations and advantages

of individual river/lake linking’s as intra and inter

country projects.

In India more than40 years back Ganga-Cauvery link

was proposed to enable surplus water from Ganga to

augment supplies in the water scarce southern and

western parts of India. This project was also proposed to

control floods in the northern India. Even though this

project was never given due importance, due to various

bottlenecks, including problems due to significant

topographic variations, lack of needed data to overcome

segment wise changes in hydrological factors and

various river basins dynamics and environmental

setbacks. Since the last one decade many proposals

surfaced supporting river linking. Former Prime

Minister Atal Bihari Vajpayee is credited with giving

the interlinking programme a big push in October 2002,

though the idea can be traced back to the late 19th

century and Arthur Cotton, the Madras Presidency

engineer who first conceived the plan to improve inland

navigation in peninsular India. In 1973, then Union

minister for water resources K.L. Rao proposed the

Ganga-Cauvery (Kaveri) Link. The idea resurfaced,

bigger in scope, in the late 1970s as the Garland Canal,

proposed by engineering consultant Dinshaw Dastur.

The government made its first serious move in 1980,

when the ministry of water resources framed the

National Perspective Plan, which proposed inter-basin

transfers. In 1982, the National Water Development

Agency was set up to carry out pre-feasibility studies,

which formed the basis of an interlinking plan. In 1999,

a national commission was set up to review these study

reports. It was of the view that there was "no imperative

necessity for massive water transfers in the peninsular

component" and that the Himalayan component would

"require more detailed study”. Interlinking got a boost

when then President APJ Abdul Kalam made a passing

reference to the need for finding a solution to

simultaneous floods and droughts in his address to the

nation on the eve of Independence Day in

2002.Recently, the justices of the Supreme Court

decided that interlinking was a good idea and forced the

government to get moving on the plan. Khagaria is one

of three districts-along with Samastipur and Begusarai

(Bihar state)-where India's first river-linking project will

take place. The hope is that the rivers will help drain

away the floodwaters and provide irrigation in the dry

mailto:[email protected]


P. R. REDDY AND D. VENKAT REDDY

International Journal of Earth Sciences and Engineering

ISSN 0974-5904, Vol. 07, No. 01, February 2014, Editorial Note

season. The river-linking idea is one that's been

knocking around for a long time - only natural in a

country that can suffer from drought and flooding

simultaneously and repeatedly to such shattering effect.

But the many critics of the programme call it bad

science on a grand scale that will cause the irreversible

destruction of lives and property, while bringing about

environmental catastrophe. They say there is no

understanding or clarity about the likely impact of

interlinking on the air and water, biological diversity

and socio-economic fabric of the area. An

environmental protagonist, who works in the areas of

flood management and people's rights, describes the

interlinking of rivers as a "mad project". He argues that

the rivers in the region are already interlinked and

artificial intervention is going to give rise to other

problems as it runs counter to hydrological norms. "You

can't play with the environment and win. We have

already seen what building of barrages has done in

Uttarakhand," where deadly floods occurred in 2013.

"Do you think that a barrage can restrict a river in full

flow? Have they forgotten what happens on the Kosi,"

the worried expert says. The Kosi symbolises

engineering led solutions to flooding that don't take into

account the knowledge that farmers have gained from

centuries of working in the land. So, while agriculturists

welcome low-intensity flooding that regenerates the soil

with the silt that the water carries, engineers build

embankments, barrages and dams in a bid to halt the

water in its tracks. Nature has altered the Kosi's course

over the centuries, and the people who live there have

tried to adjust to this. But since the days of the British

Raj, engineers have sought to intervene in a bid to try

and make life more settled for them. Unfortunately, it

hasn't worked. It is pointed out that the 2008 flash flood

on the Kosi happened as no proper assessment of the

flood impact on the eastern and western canals, built

between 1954 and 1960, was made .When magnitude of

floods attain unprecedented proportions the manmade

dams and canals collapse like a pack of cards leading to

ctstrophe. Such devastation was also witnessed on the

Krishna river in 2009.The details given above scare

everyone including engineering experts. But, we need to

find apt solutions to such setbacks, if we want a radical

change in our water management, which alone can

ensure better utilisation of available water.

While we support any meaningful technological

intervention to address water management and water

related natural disasters we feel the national river

linking mega project needs a well-planned strategy from

planning stage till completion of the project, constantly

taking mid-course corrections to ensure quality control .

It is essential that small scale linking be taken up in

earnest to have firsthand knowledge of probable

setbacks due to linking mechanism. This is paramount

as the segmentation of the country in to smaller states

can pose river water distribution problems, leading to

some additional bottlenecks. As such, the Central

Government has to view at implementation of any river

linking as national asset and ensure co-operation

between various stake holders. In the next sub section

we cover some specifics. This editorial is not aimed at

detailed exposition of the river linking project. It is

basically aimed at in bringing in to light some specific

advantages and disadvantages due to river linking, so

that learned and young researchers can come out with

new strategies to make this important project viable and

useful.

Reasons and motivations

In India the rainfall over the country is primarily

orographic, associated with tropical depressions

originating in the Arabian Sea and the Bay of Bengal.

The summer monsoon accounts for more than 85 per

cent of the precipitation. The uncertainty of occurrence

of rainfall marked by prolonged dry spells and

fluctuations in seasonal and annual rainfall is a serious

problem for the country. Large parts of Haryana,

Maharashtra, Andhra Pradesh, Rajasthan, Gujarat,

Madhya Pradesh, Karnataka and Tamil Nadu are not

only in deficit in rainfall but also subject to large

variations, resulting in frequent droughts and causing

immense hardship to the population and enormous loss

to the nation. The water availability even for drinking

purposes becomes critical, particularly in the summer

months as the rivers dry up and the ground water

recedes. Regional variations in the rainfall lead to

situations when some parts of the country do not have

enough water even for raising a single crop. On the

other hand excess rainfall occurring in some parts of the

country creates havoc due to floods.

Irrigation using river water and ground water has been

the prime factor for raising the food grain production in

our country from a mere 50 million tonnes in the 1950s

to more than 200 million tonnes at present, leading us to

attain self-sufficiency in food. Irrigated area has

increased from 22 million hectares to 95 million

hectares during this period. The population of India,

which is around 1050 million at present, is expected to

increase to 1500 to 1800 million in the year 2050 and

that would require about 450 million tonnes of food

grains. For meeting this requirement, it would be

necessary to increase irrigation potential to 160 million

hectares for all crops by 2050. India's maximum

irrigation potential that could be created through

conventional sources has been assessed to be about 140

million hectares. For attaining a potential of 160 million

hectares, other strategies shall have to be evolved.

Floods are a recurring feature, particularly in

Brahmaputra and Ganga rivers, in which almost 60 per




cent of the river flows of our country occur. Flood

damages, which were Rs. 52 crores in 1953, have gone

up to Rs. 5,846 crores in 1998 with annual average

being Rs. 1,343 crores affecting the States of Assam,

Bihar, West Bengal and Uttar Pradesh along with untold

human sufferings. On the other hand, large areas in the

States of Rajasthan, Gujarat, Andhra Pradesh,

Karnataka and Tamil Nadu face recurring droughts. As

much as 85 percentage of drought prone area falls in

these States. One of the most effective ways to increase

the irrigation potential for increasing the food grain

production, mitigate floods and droughts and reduce

regional imbalance in the availability of water is the

Inter Basin Water Transfer (IBWT) from the surplus

rivers to deficit areas. Brahmaputra and Ganga

particularly their northern tributaries, Mahanadi,

Godavari and West Flowing Rivers originating from the

Western Ghats are found to be surplus in water

resources. If we can build storage reservoirs on these

rivers and connect them to other parts of the country,

regional imbalances could be reduced significantly and

lot of benefits by way of additional irrigation, domestic

and industrial water supply, hydropower generation,

navigational facilities etc. would accrue.

National River Linking Project in India

The National River Linking Project (NRLP) is designed

to ease water shortages in western and

southern India while mitigating the impacts of recurrent

floods in the eastern parts of the Ganga basin. The

NRLP, if and when implemented, will be one of the

biggest inter-basin water transfer projects in the world.

Some experts suggest transferring of water from high

precipitation western flank of Sahyadris through tunnels

to augment Godavari and Krishna rivers (This is not

included in NRLP). Cost of the project was estimated at

Rs. 5,60,000 crores.However,the true cost can be

known only when the detailed project reports of the 30

river link projects are drawn up. At Rs. 5,60,000 crore,

it's the mother of all projects. It will connect the rivers

in the north with those of the south through a network of

canals. Water from the Brahmaputra will flow into the

Ganga, which in turn will be connected to the Mahanadi

and Godavari. Godavari will be linked to Krishna, then

to Pennar and Cauvery. Similarly, Narmada will flow

into the Tapi and Yamuna into the Sabarmati. This

grand inter-basin transfer is slated to be completed by

2016. "It is a win-win situation for all - states with a

problem of floods and drought," promises Suresh

Prabhu, chairman of the task force for linking rivers.

Radha Singh, D-G, National Water Development

Agency says: "The 30 feasibility studies conducted so

far have indicated that the project is viable since the

canals will be based on gravity, and have storage

facilities."

Coping with annual floods and droughts, both occurring

at the same time indifferent parts, has been a major

concern for India over the years. These concerns are

more acute today as the growing population and the

resultant increase in water demand place a heavy burden

on the unevenly distributed water resources, and also

cause huge economic losses to the financially

vulnerable groups of the population. Additionally, there

is a huge demand to enhance and diversify food

production. Designed to address these issues, the

National River Linking Project proposes to transfer

water from the potentially water surplus Himalayan

rivers to the water-scarce river basins of western and

peninsular India. The NRLP will build 30 river links

and approximately 3000 storages to connect 37

Himalayan and peninsular rivers to form a gigantic

south Asian water grid. Environmentalists questioned

the ecological cost of large dams, while the NGOs and

civil society probed the social cost of people

displacement. However, much of the arguments for and

against the project have little analytical rigor. The

concept of linking of rivers or inter-basin transfer of

water is essentially based on the availability of surplus

of water in the donor river especially at the point of

diversion to the deficit river basin. The surplus or deficit

in a basin is determined on the basis of availability at

75% dependability, import, export, and existing and

future needs. A river basin is said to be reasonably in

surplus of water, if the surplus water is available after

meeting the irrigation needs of at least 60% of the

cultivable area in the basin. Only this water from such a

basin can be diverted to deficit basins. In the

recipient/deficit river basin, it is proposed that, at least,

30% of the cultivable area is covered under irrigation.

This is one of the most effective managements of

surface water resources, as according to protagonists, it

is an economically viable, technically feasible and

environmentally sound and viewed as the future main

stay for the sustainable development of any region

confronting water deficit. On this basis, The National

Water Development Authority (NWDA) after a

thorough study indicated that Himalayan rivers,

especially, Brahmaputra and Ganga have exceedingly

surplus quantum of water and hence, proposed transfer

of water from these surplus basins to deficit basins in

peninsular region.

There is an immense pressure to share river waters

among the countries, states and regions. The political

and social issues are very important as they may decide

the fate of this kind of projects of national importance.

A pragmatic expert opined in 2004 that the linking of

rivers is more problematic for socio-economic-cultural

relations of the society. However, we need to go ahead

with the project due to various compulsions. In South-

East Asia, the Himalayan river waters are of interest, as




the Himalayan region has some of the world’s most

underdeveloped/developing countries, Bangladesh,

Nepal, India, Bhutan, Pakistan, Tibet, and China.

Construction of dams across the Himalayan rivers

Brahmaputra and Ganga and their main tributaries in

India and Nepal and interlinking of their canal system

and transfer of surplus flows of the eastern tributaries of

the Ganga to the west in addition to linking of Ganga

and Brahmaputra constitute implementation aspects of

the main concept of inter-basin transfer of water

between the countries. While providing irrigation to

additional 22 million hectares, it generates pollution free

hydro-power and will provide flood control in the

Ganga- Brahmaputra basin. Thus, Ganga-Brahmaputra

basin, and Nepal and Bangladesh would have advantage

from the project.

Linking the restoration of rivers and riparian

zones/wetlands

Floodplains are heavily impacted by human intervention

and often disconnected from the main river channel.

Restoring lateral hydraulic connectivity between

wetlands, fringe habitats and riparian land with the

adjacent river channel is extremely important to

maintain natural functioning of floodplain wetlands.

However, there is no simple solution to restoring and

rehabilitating rivers and their floodplains, particularly in

terms of long-term sustainability. Floodplains are often

the most fertile and productive part of the landscape, in

terms of both agricultural production and natural

ecosystems. Restoration projects must be able to

balance conflicting needs and interests. Flood

management is one of the most powerful drivers of

developing strategies for floodplain restoration.

Appropriate restoration management of floodplains is

vital for the conservation of unique bio-diverse systems

and for sustainable agricultural productivity. By

developing strategies that better incorporate floodplain

restoration in the context of the basin scale, it will

become more feasible to develop the most effective

restoration actions for a specific river type and location.

Within this context we must not forget that successful

natural resource management is much more than

developing good science; it requires working with

landowners, meeting deadlines, securing funding,

supervising staff, and cooperating with politicians.

Furthermore, the benefits of floodplain restoration must

be equally demonstrated for multiple purposes including

a range of ecosystem services. In view of its importance

the NRLP should include, as an important component of

the project, floodplain restoration. As such, while going

into specifics of 30 Linkings the concerned should

explore the various interactions associated with

floodplain dynamics. We can learn from European

initiative, through case studies, in exploring the various

approaches that have been taken across Europe to

forward the restoration of the fragile and important

ecosystems in the context of current European

environmental policy and directives.

Proposed River Linking under NRLP

Major Bottleneck

Bangladesh has fears and is creating disinformation in

the world forum, that the mega projects, to be undertaken

in India for diversion of waters from Ganga, would cause

water scarcity in Bangladesh. But the flow data in Ganga

and the quantum of water to be diverted reveal that




Bangladesh has unnecessary fears. At least the transfer

of water from Ganga to peninsular component does not

affect the water status of Bangladesh. However, this has

become a bone of contention for river-sharing between

the countries. It is time we sit together and resolve the

issue, instead of allowing it to remain as an irritant.

Benefits

Irrigation

By linking of rivers vast amount of land areas which are

not used for agriculture can be converted into fertile.

Flood prevention

During heavy rainy seasons some areas can experience

heavy floods while other areas might be

experiencing drought like situations. With network of

rivers this problem can be greatly avoided by

channeling excess water to areas that are not

experiencing a flood or are dry. This works similar to

canal system in Netherlands to channel excess water

from sea.

Generation of electricity

With new canals built, feasibility of new DAMS to

generate hydroelectric power becomes a possibility. It

expects to add 34,000 MW of hydro power to the

national grid (clean energy).This in turn will create

employment and boost crop output and farm income.

Navigation

Newly created network of canals opens up new routes

and ways and routes of water navigation, which is

generally more efficient and cheaper compared to road

transport.

Issues and Concerns

Ecological issues

Major concern being the argument that rivers change

their course in 70–100 years and once they are linked,

future change of course can create huge practical

problems for the project

Environment

Canals will pass through national parks and sanctuaries.

The ministry of environment has not given permission

even for carrying out initial surveys. R K Pachauri of

Tata Energy Research Institute says, "The government

needs to answer how many people will be displaced by

dams and canals? What about the flora and fauna? How

will the soil be affected?"

Excess water during monsoons

Sunita Narain of the Centre for Science and

Environment says, "Monsoons happen all over India at

the same time. When there's excess water in the

Brahmaputra, there'll be excess water in Ganga and

Mahanadi too. Interlinking can cause storages to

overflow and cause flooding.

Aqua life

A number of leading environmentalists are of the

opinion that the project could be an ecological disaster.

There would be a decrease in downstream flows

resulting in reduction of fresh water inflows into the

seas seriously jeopardizing aquatic life. Even though

arresting the river flow into sea to meet inland needs

could be excused ( if the environmental degradation is

avoided while designing and executing the storage and

distribution facilities), we need to keep in mind the

probable influence of such an exercise in the long run

on the coastal and ocean ecosystems.

Deforestation

Creation of canals would need large areas of land

resulting in large scale deforestation in certain areas.

Areas getting submerged

Possibility of new dams comes with the threat of large

habitable or reserved land getting submerged under

water.

Displacement of people

As large strips of land might have to be converted to

canals, a considerable population living in these areas

must need to be rehabilitated to new areas. Such

rehabilitation is fraught with many problems, especially

when tribal segments are involved. Any

misunderstanding or absence of a proper mechanism in

rebuilding new dwellings can lead to catastrophic

results, and could be used by extremists in destabilising

peace and tranquillity.

Global Resume

Even as India has been procrastinating, the rest of the

world has gone about inter-basin water transfer (IBT)

projects at a brisk pace during the past 50 years or so.

Global and local opposition now withstanding, China

has steadfastly stayed course on its own scheme of

transferring 48KM3 of water from Yangtze to the

Yellow River to improve water availability in dry plains

of North china. Elsewhere in the world many IBT

projects have faced a variety of problems and produced

some unwanted side effects; however, in overall terms,

most have turned out to be beneficial. Even a wary

global environmental review of IBTs - which advocates

using precautionary principle, concluded that: “In many

parts of the world, water transfers have become the

lifeblood of developing and extant human settlements,




for which no alternative is currently perceived to be

available.”

Integrated River Basin Management (IRBM)

"Integrated river basin management (IRBM) is the

process of coordinating conservation, management and

development of water, land and related resources across

sectors within a given river basin, in order to maximise

the economic and social benefits derived from water

resources in an equitable manner while preserving and,

where necessary, restoring freshwater

ecosystems." Since Indian River Linking Project, in its

present form, has some limitations it is advisable to take

up simultaneously IRBM of individual river basins and

then link with adjacent river basin to maximise benefits.

World over some significant studies have been taken up.

The outcome of these initiatives, even though literally

not projected as conventional River Linking Projects are

useful in strategic planning of sector or segment wise

small scale River Linking.

Before reviewing them, one should be aware that

although all have produced substantial outputs, none of

them presents a complete, functioning IRBM process

because few, if any, such cases yet exist.

A new discipline

IRBM is a very new discipline that requires time to plan

and begin implementing, let alone to reach the stage of

maturity when tangible, on-the-ground benefits are seen

at basin-wide level. Instead, each case study

demonstrates the use of one or more particular

approaches, tools or processes intended to promote and

catalyse wider IRBM schemes within the respective

basin.

Projects in different stages of development

Not all of the projects are at the same stage. Some, such

as the Danube and Everglades, reflect long-term

engagement of WWF and its partners over a decade or

more, and in these can be seen the promise of basin-wide

achievements. Others are working their way towards the

river basin scale, perhaps having started out as smaller

site-specific or issue-specific projects. WWF offers these

case studies as food for thought and as experiences from

which others may learn and benefit. The IRBM experts

do not claim to have all the answers, nor do they in any

way claim that this represents a definitive text on IRBM.

There is still much to be learned. They hope, however,

that this work will provide some guidance, stimulate

some ideas, and spur some action to make IRBM a

reality in more basins in more parts of the world.

Case study layout

Each of the case studies presented here from river basins

across the world follows a standard format, including:

Danube

The Danube basin, covering 817,000km² - about one-

third of continental Europe outside Russia - is the most

international river basin in the world, extending over all

or part of the territories of 18 countries.

Europe's 2nd longest river, the Danube River itself

crosses ten countries and is Europe's second longest

river after the Volga, flowing over 2,857 km from

Germany's Black Forest to the Romanian and Ukrainian

Danube Delta on the shores of the Black Sea. The

Danube is also Europe's only major river that flows west

to east, from the current Member States of the European

Union through the former eastern bloc countries of

central and eastern Europe, many of which are now

prospective EU members. The European Commission

recognizes the Danube as the "single most important

non-oceanic body of water in Europe" and a "future

central axis for the European Union".

Socio-economic importance

The main economic uses of the Danube are:

domestic/drinking water supply

water supply for industry

water supply for agriculture

hydroelectric power generation

navigation

tourism and recreation

waste disposal (both solid and liquid wastes)

fisheries

In addition, the Danube's remaining floodplains provide

a range of economically important 'ecological services',

such as water quality regulation and flood control.

The Everglades- U.S.A.

It is a rain-fed, flooded grassland/wetland that once

extended from Lake Okeechobee in the north to Florida

Bay in the south. The slow-moving, shallow water

flowed as vast sheet through varied landscapes from

sawgrass marshes to mangrove estuaries, ending its

journey by mixing with the seawater of Florida Bay.

Today, half of the original Everglades have been

drained. Large quantities of fresh water have been

diverted to drain land for agriculture and to provide

flood control for coastal cities. Almost 2.5 billion cubic

metres (2 million acre-feet) of water are diverted from

the natural system annually, damaging the ecology of

the coastal estuaries. Polluted and nutrient-rich water

flowing into Florida Bay is adversely impacting marine

habitats including fragile coral reefs. Saltwater intrusion

has become a serious problem, making it necessary to

drill deeper freshwater wells inland away from coastal

urban areas. Ironically, this has led to water-use

restrictions in one of North America’s wettest regions.





The Everglades support major industries and provide

South Florida’s drinking water, supporting the explosive

development of one of the fastest growing and

economically dynamic regions in the United States. Due

to massive diversions of fresh water, largely for flood

control in areas that were formerly wetlands, the

remarkable biological diversity and productivity of the

entire South Florida ecosystem is at risk. Yet this

diversity and productivity are at the very heart of the

region’s vital multibillion-dollar tourism and fishing

industries. With South Florida’s population projected to

double by 2050, a robust system of sustainable use is

required if the Everglades are to survive the growing

human pressure.

Great Barrier Reef – Australia

Thirty-four sub-basins (or catchments) form the ‘basin’

of the Great Barrier Reef. These cover an area of

370,000km² and extend from the tip of Cape York,

south to the Mary River near Hervey Bay. The largest of

the developed catchments is the Fitzroy, at just over

150,000km², while the Mossman River is the smallest at

just 490km². Eight catchments are in relatively pristine

condition, while the Great Barrier Reef Marine Park

Authority has set pollutant reduction targets for the

remaining 26.


Sugarcane is the major crop grown along the low lying

and ecologically sensitive areas adjacent to the Great

Barrier Reef. Grazing land, supporting an estimated 5

million cattle, occupies over 80 per cent of the reef's

catchment. Horticulture (the growing of bananas, other

tree crops such as mangos, and vegetable crops such as

tomatoes) is growing rapidly along the northern

coastline, and aquaculture is also a fast-developing

industry. There are currently 40 licensed aquaculture

operations adjacent to the Great Barrier Reef Marine

Park, including 25 marine prawn farms covering around

542ha.The Reef is also a tourism 'hotspot' such that

many people living in the coastal towns and cities rely

on the Reef for their daily income. However, large-scale

tourism also brings with it the impacts of coastal

development and associated problems of pollution and

sewage disposal.

Gwydir-Australia

The 200,000ha Gwydir wetlands are a terminal inland

delta of the Gwydir River. These ecologically important

wetlands lie in the heart of one of Australia’s largest

agricultural areas, and have been suffering for the past

20 years due to water extraction for irrigation, most

notably for cotton growing.The Gwydir River forms

part of the Murray- Darling Basin which drains

approximately one seventh of the landmass of Australia.

Central to this case study are the floodplain wetlands

located along 95km of the Gingham and Lower Gwydir

watercourses west of Moree in northern New South

Wales.


Following completion of Copeton Dam on the Gwydir

River in 1976, irrigation schemes grew rapidly to the

point where demand outstripped the capacity of the dam

by almost one-fifth. The upstream diversion of water for

irrigation had a significant effect on downstream

pastoralists, whose grazing productivity declined by up

to 73%.The drying-out of wetlands also saw a marked

increase in cereal cropping on these areas, resulting in

further wetland loss.

Kafue Flats-Zambia

Kafue Flats are the vast, open floodplain of the Kafue

River, covering some 6,500km² within the wider basin

of the Zambezi River.


The area is important for fishing, cattle grazing,

sugarcane farming, and production of hydroelectric

power. Zambia's water and hydroelectric power

potential are of great importance to the national

economy and to the regional economy of southern

Africa. The Kafue Gorge hydroelectric power plant,

situated at the eastern end of the Kafue Flats, is the

country's largest power station, providing more than

50% of Zambia's electricity needs. A surplus of 431

MW is exported to neighbouring countries, such as

Zimbabwe and South Africa. To keep pace with

demand, the Kafue Gorge power plant has needed more

water than was available from the Kafue Gorge Dam.

Consequently, a second storage reservoir (the Itezhi-

tezhi Dam) was constructed at the western end of Kafue

Flats. This allows for the release of sufficient water to

maintain maximum power generation throughout the

year. On the south-eastern side of Kafue Flats, near the

town of Mazabuka, there are several sugarcane farms,

each of which cultivates huge areas of land. These farms

produce the majority of Zambia's sugar for local use and

export. Each farm relies heavily on water from the

Kafue River for irrigation, while nutrient-rich effluent is

discharged back into the river, contributing to the

proliferation of many aquatic plants, including the

problematic water hyacinth Eichornia crassipes.

Traditionally, the people of Kafue Flats have made a

living by fishing and grazing livestock. Until recently,

the area was sparsely populated but this is changing as

many people arrive in search of work, for example on

sugarcane estates. This has promoted illegal hunting and

overfishing. As a result, certain parts of the Flats are

suffering from increasing human pressure.




The Kinabatangan River-Malaysia

It is the largest and longest river in the Malaysian state

of Sabah. It has a main channel length of about 560 km,

a catchment area of about 16,800 km² and covers almost

23% of the total land area of Sabah. Mean annual

rainfall in the catchment is between 2,500 mm and

3,000 mm. Flooding is common along the

Kinabatangan, with major flood events causing serious

damage to livelihoods and property in 1963, 1967, 1986

and 1996. The Kinabatangan floodplain is the largest

remaining forested floodplain in Sabah and the lower

stretches of the Kinabatangan River contain some of the

few surviving freshwater swamp rainforests and oxbow

lakes in South-East Asia. These evergreen swamp

rainforests are of global significance for biodiversity

conservation.


The river, used for transport, trade and communication,

has been the lifeblood of local people for centuries.

Forest products such as edible birds’ nests and bees'

wax, elephant ivory and hornbill casques were once

traded. Nowadays there are about 20 palm oil mills in

the Kinabatangan basin, which process the produce

from rapidly expanding oil palm plantations. The oil is

used in the production of margarine, soap, livestock

feed, lubricants, and many other industrial and

household products.

Large-scale commercial logging and small-scale

farming began along the Kinabatangan in the early

1950s. This provided the people of Sabah with income

and employment. Several forest reserves were created in

the 1970s, but these were quickly reallocated for

agricultural use. The lower Kinabatangan, with its

unique biodiversity, is also increasingly recognized as a

destination for ecotourism and local people are

becoming involved in this activity.

La Cocha- South America

La Cocha (which simply means 'lake') is a high Andean

lake located on the eastern slopes of the southern Andes

of Colombia, just north of the border with Ecuador. It

forms part of the upper watershed of the Guamués

River, an important tributary of the Putumayo and San

Miguel Rivers, themselves major tributaries of the

Amazon basin. The lake and the immediately

surrounding land lie between 2,700m and 2,800m above

sea level and cover a total area of approximately

39,000ha, comprising the largest wetland system in the

Colombian Andes. The water-body itself is some 13km

long and 6km wide. The basin includes wetlands, cloud

forest and 'paramo' (high montane grassland).


Small farms in the area produce milk, potatoes and other

vegetables. Charcoal production, generally undertaken

by the poorest farmers and those members of the

community without any land of their own, is an

important but unsustainable economic activity, resulting

in progressive degradation of forest cover and resources.

Lake Chad-Africa

As big as the Caspian Sea as recently as 8,500 years

ago, Lake Chad is now Africa's fourth largest lake, with

a maximum extent of 25,000km². One of three major

wetlands located within the Sudano-Sahelian zone (the

others being the Niger River Inner Delta in Mali, and

the Sudd Swamps in Sudan), Lake Chad is rather

shallow and has been particularly susceptible to the

increasing variability and irregularity of rainfall during

the last 40 years. It has fluctuated greatly during this

period, shrinking by up to 80% in 1985, but reaching

19,000km² once more in 2001.The River Chari - along

with its tributary, the Logone - provides 90% of the

inflow to the lake, while the remaining 10% comes from

the Komadougou-Yobe River system. Three-quarters of

the water entering the lake north of N'djamena originate

from headwaters in the Central African Republic and, to

a lesser extent, Cameroon.


The Lake Chad basin supports more than 20 million

people. The local economy in the upper part of the

catchment is based on fishing, agriculture and

pastoralism. However, people living around the lake

lack access to safe drinking water and proper sanitation.

More than 150,000 fishermen live on the lake's shores

and its islands. The current estimate of annual fish

production from the lake is 60,000 to 70,000 tonnes.

However, as a result of environmental changes since the

1970s, including fluctuations in lake level, there have

been considerable changes in the fish fauna. These

include high mortality, the disappearance of some open-

water species, and the appearance of species adapted to

swamp conditions in areas where they were previously

unknown. The raising of cattle, sheep and camels - by

local as well as nomadic herders - is also economically

important, together with cultivation of some traditional

crops. The most common system is lake-bottom

cropping or receding moisture cultivation, which has

been a response to the contraction of Lake Chad.

Villagers have shifted from relying entirely on fishing,

to farming the emergent lake floor as flood water

recedes. A few large-scale irrigation schemes (polders)

developed on some parts of the lake shore have proven

totally unsuited to the hydrological, climatic and

cultural conditions in the Lake Chad region, and can be

considered as complete failures. Though still quite

marginal, the production of spiruline (blue algae) seems

to be gaining economic importance.




In addition to direct support for livelihoods, the lake

also plays an important socio-economic role in

regulating annual water supply, recharging

groundwater, and helping to control flooding.

The Loire-Europe

The Loire has frequently been characterized as "the last

wild river in western Europe" owing to the relative

absence of large dams and the consequent semi-natural

condition of the river, notably in its upper reaches. The

main channel is more than 1,000 km in length and the

total hydrographic network extend to more than 135,000

km. The basin covers a total area of 155,000 km² or

22% of French territory.


The Loire basin has more than 11.5 million inhabitants

but is markedly rural in character, with more than one-

third of communities having fewer than 400 inhabitants.

The basin is extremely important for farming,

supporting two-thirds of livestock raising and half of all

cereal production in France. Some 350,000 ha of

farmland in the basin are irrigated. The Loire itself is

used for navigation, generation of hydro and nuclear

power from 38 dams and four power stations, and

recreation. The estuary and its shoreline are important

for fishing, shellfish farming and tourism, and there is a

major commercial port at Nantes, which has caused

severe damage to the Loire estuary's ecology.

The Prespa-Balkans

The Prespa basin, covering a total area of 2,519 km²,

contains the lakes Mikri ('small') Prespa and Megali

('large') Prespa and is situated in the Balkans, straddling

the borders of Albania, Greece, and FYR of

Macedonia. The basin has no surface outflow, with

Mikri Prespa flowing into Megali Prespa, which in turn

flows into the Ohrid Lake basin via subterranean

channels and from there to the Adriatic Sea. The area is

famed for its natural beauty, high biodiversity, and

outstanding cultural values (e.g. Byzantine monuments,

traditional architecture, unique artisanal fishing

methods).Significant parts of the lakes and adjoining

wetlands in the territories of Greece and FYR of

Macedonia are designated as Ramsar Sites.


Around 5,000 people in the Albanian part of the basin

are engaged mainly in subsistence farming, the former

collective agricultural system having been abandoned

since the collapse of the totalitarian regime. Basic

infrastructure has deteriorated and communities are

under strong economic pressure to overexploit natural

resources. Rural depopulation and unemployment have

characterized the region, especially in Greece. However,

75% of the population (about 1,200 people in 13

villages) in the Greek sector continue to rely on

agriculture, especially mono-cultivation of beans, for

their livelihoods, though increasing tourism offers

alternative income generation.

The portion of the basin within the territory of FYR of

Macedonia is the most densely populated. Here, over

17,500 inhabitants live in some 40 settlements, though

strong rural-urban migration is resulting in an ageing

and declining population. Fruit growing is the major

activity, while the manufacturing sector employs about

3,000 people.

The São João River –Brazil

The basin covers 2,190km² of the northern part of Rio

de Janeiro State, Brazil. It is 120km long and flows

from mountains and hills to a broad coastal plain with

numerous ponds and lagoons, including the 220km²

Lake Araruama, the largest coastal saline lagoon in

Brazil. The marine zone adjacent to the river mouth is

notable for resurgence of nutrient-rich marine water,

which supports a rich fish fauna and the southernmost

occurrence of coral in Brazil.


The basin contains eight municipalities and about

100,000 people live in the region.

The main economic activities are real estate, beach

tourism and fishing in the coastal strip, and agriculture

and tourism (ranch-style hotels) in the rural hinterland.

The extraction of salt from Lake Araruama, formerly an

activity of great importance, is nowadays in decline.

And during the 1970s, the government dammed the São

João River at Juturnaíba Lake, which was enlarged from

8km² to almost 40km², thereby creating a large

reservoir. This became the main source of water for

several cities, including some of Brazil's top beach

resorts. During the tourist season, the local population is

at least doubled.

In addition to the above the following two Projects have

yielded good results:

Colorado Big Thomson, USA diverts about

0.284km3/ annum of water from the upper reaches

of the western flowing Colorado river and sends

eastwards into the south Platte River Basin, which is

a part of the Mississippi-Missouri basin. Completed

in 1957.

Lesotho Highlands Water Project, South Africa.

Completed in 2004 diverts 750m3/ annum of water

from Lesotho to South Africa

The lessons learned from individual basins have been

aggregated and synthesised by the concerned to draw

general lessons that can be of value in most places,




under most circumstances. These lessons could be used

in articulating individual river linking projects under

NRLP.

Conclusions

It is evident from the available information, given above

down loading from Wikipedia , Indian National River

Linking Plan and other documents, that the river linking

project has some specific advantages and significant

problems. As such all the experts belonging to

irrigation, agriculture, environment, tribal welfare

depts., states and Central Govt administration wings, sit

together and select specific links that are viable and less

problematic, instead of stalling the project under one

pretext or the other. At the same time the committed

technical experts and administrators should come out

with area/ individual link specific details to address the

doubts expressed by environmental protagonists and

villagers whose lands would be submerged. Since global

warming related monsoon aberrations are going to be

more frequent, it is essential for one and all to develop

designs/ models that can overcome area specific

bottlenecks. The routinely implemented major irrigation

project norms are not sufficient to achieve success. It is

also essential to ensure strategic outlets to enable excess

water to go to the seas to ensure healthy coastal and

marine ecosystems.

Irrespective of various bottlenecks, we need to gear up

to meet water demand. It is clearly established that ever

increasing population growth will not allow water

scarcity problem to improve on its own. There are

several concerns raised against undertaking such a

mammoth project like land acquisition, daunting cost,

disturbance of natural river course, population

displacement and conflicts amongst Indian states and

neighbouring countries. So like any major project this

project comes with its cons but it is up to us to weigh

the pros and cons and take an informed decision. By

2025 India will be a water starved nation if adequate

steps are not taken. Alternative ideas like improving

water harvesting techniques, efficient irrigation and

proper waste management have been proposed but they

are all at a very low scale and none will make a

significant impact as per studies. Disregarding whether

India goes through with this project or not, change is

inevitable. With ever-growing pressure of global

warming and increasing human population, water

scarcity problem will not improve on its own and we

have to go for a radically innovative technological

intervention to circumvent this problem. The River

Linking, if implemented properly, will help us to a great

extent in meeting the water demand.

Grandiose projects have a way of coming unstuck in

this country. Let's hope this one proves the skeptics

wrong

Acknowledgements

We have structured major part of this editorial using

available information on internet. We have added some

linking sentences and comments to make the

presentation meaningful. As such, we are thankful to

various organisations and a large number of experts for

making available needed information.

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ISSN 0974-5904, Volume 07, No. 01

February 2014, P.P.190-194

#02070128 Copyright ©2014 CAFET-INNOVA TECHNICAL SOCIETY. All rights reserved.

Geochemical Investigations on Thermal and Cold Springs at Dumka

District, Jharkhand, India

HEMANT K. SINGH1, D. CHANDRASEKHARAM

1, TRUPTI G

.1 AND B. SINGH

1, 2, 3

1Department of Earth Sciences, Indian Institute of Technology Bombay, Mumbai-400067, INDIA

2IITB-Monash Research Academy, Indian Institute of Technology Bombay, Mumbai-400076, INDIA

3Civil Engineering Department, Monash University, Clayton, Melbourne-3800, AUSTRALIA

Email: [email protected], [email protected], [email protected], [email protected]

Abstract: There are various thermal and cold springs located in Dumka district of Jharkhand, India. These springs

are issuing through the Chotanagpur Gneissic Complex (CGC) and Rajmahal trap. Surface temperatures of the

thermal springs range between 42° to 70°C and are near neutral to moderately alkaline(pH = 6.9 - 9.5) in nature.

Position of thermal water in Piper diagram suggests that the thermal springs are Na-Cl type and the chemistry of

thermal springs is compatible with the host rock of the area. Cold springs of the area are near neutral (pH = 7.4-7.5)

in nature and fall in Ca-HCO3 field in Piper diagram, indicating that the circulation of the cold springs is through

sedimentary formation. Estimated reservoir temperature based on chemical geothermometers ranges between 92° to

138°C; indicating that these thermal springs are suited for low enthalpy geothermal system.

Keywords: Dumka thermal springs, thermal water geochemistry, reservoir temperature.

1. Introduction:

Large number of thermal and cold springs is located in

Dumka district of Jharkhand, India, in a broad N-S belt

east of Dumka, in the Rajmahal Volcanic belt. These

springs lie in line with the well-known Bakreshwar

springs further south in the state of West Bengal, known

for the Helium emanations from them [1, 2]. Most of the

springs circulate through the Chotanagpur Gneissic

Complex (CGC), whereas some of them propagate

through the Rajmahal trap (Fig. 1). Thermal springs

show surface temperatures variation from 42° to 70°C

while the cold springs of the area have surface

temperature between 24° to 27°C close to the average

ambient temperature of 30°C.

Fig1: Regional geological setting and location of the study area (modified after [3, 4, 5, 6, and 7])





191 HEMANT K. SINGH, D. CHANDRASEKHARAM, TRUPTI G AND B. SINGH


ISSN 0974-5904, Vol. 07, No. 01, February, 2014, pp. 190-194

Present investigation is based on establishing the

geochemical evolution of Dumka district thermal

springs and estimation of the reservoir temperature.

2. Geology of the area:

The study area falls in the Chotanagpur Gneissic

Complex (CGC) of the eastern Indian Peninsular region

that, has been subjected to major tectonic activities at

different cycles of plate movements with intervening

periods of isostatic readjustment during Precambrian [4]

to Cenozoic time [8, 9, 10, 6]. Major tectonic features of

Jharkhand includes Gondwana faults that may have a

Precambrian ancestry, faulting related to the

distensional tectonics associated with Rajmahal

Volcanism and the back thrust from the Himalayan

collision zone [11]. The most striking structural feature

of the area is a N–S trending weak sheared zone,

marked by repeated silicification and brecciation, that

can be traceable over 1.4 km from Gohaliara to

Tantipara and further north (Fig. 1). The granite gneiss

is tectonically deformed giving rise to anticlinal and

synclinal folds [1]. A large number of dolerite dykes

transect the granites trending parallel to the regional

fractures (Fig. 1). Considering the trends of silicified

zone, fold axis, joint planes and alignment of these

thermal springs, it may be assumed that the emergence

of hot water and gases is controlled by intersecting

fractures trending N–S and NW–SE, as well as NE–SW.

3. Methodology and results:

Representative water samples were collected from

Dumka area (Fig. 1). All the water samples were

collected in 2 sets. One set of water samples was

acidified with HNO3 onsite and the other set was

stored at a lower temperature for future analysis [12,

13]. Water samples from the study area include

samples from thermal springs, bore wells, and cold

springs. The pH and temperature measurements

were determined in the field itself using ORION pH

meter. Water samples were analyzed for major

cations and anions concentration. Cations and silica

were analyzed using ICP-AES. Sulphate

concentration was measured with the aid of UV-

visible spectrophotometer, alkalinity by H2SO4

titration and chloride using ion selective electrode

method (Table 1). These analyses were done as per

the standard procedures [14].

Table 1: Data of water samples collected from Dumka area, analysis for the major ions (concentrations in mg/L)

*Sr No. pH °C Na K Ca Mg Cl HCO3 SO4 SiO2

1 9.2 62 90.9 1.3 1.2 0.01 55.1 80.0 48.8 72.5

2 7.5 64 93.2 1.2 1.1 0.01 58.5 85.0 45.4 71.5

3 7.3 42 96.0 2.4 3.0 0.30 47.9 145.0 37.6 95.4

4 7.5 62 99.5 1.6 1.2 0.01 62.9 60.0 78.9 70.5

5 7.8 25 10.9 1.7 15.3 5.20 6.1 90.0 1.6 15.5

6 6.9 42 118.9 3.5 2.4 0.50 82.4 140.0 26.3 88.5

7 9.5 70 102.2 2.1 1.3 0.01 97.5 45.0 41.4 98.6

8 9.3 68 99.4 1.4 1.5 0.01 95.0 35.0 45.6 101.2

9 7.8 26 43.2 1.5 31.7 6.30 30.5 200.0 4.3 74.2

10 7.1 27 44.7 1.6 21.3 2.90 50.0 115.0 3.7 66.0

11 7.4 25 20.9 3.3 40.1 15.10 7.7 225.0 5.7 45.6

12 7.5 27 19.5 0.9 21.5 7.50 4.7 130.0 9.6 31.1

*1-4: Bara thermal spring; 5: Bara river water; 6: Lau-Lau-Dha or Shetpur thermal spring; 7-8: Tantaloi Thermal

spring; 9-10: Tantaloi groundwater; 11: Jhawar pani cold spring; 12: Taptapani cold spring

4. Discussions:

4.1. Hydrogeochemistry:

Analyzed water samples from the study area were

plotted in Piper’s diagram (Fig. 2) to understand the

hydrogeochemistry of the waters. Groundwater and

surface water of Dumka area are near neutral to slightly

alkaline (pH=7.1-7.8 at 25°) and SiO2 content in

groundwater is 15 to 74 mg/L. The ground water is Na-

HCO3 and Ca-HCO3 type.

The thermal springs have wide range of surface

temperature from 42° to 70° C. Thermal waters of

Dumka area are near neutral to moderately alkaline

(pH= 6.9-9.5) in nature may be due to escape of CO2

from the thermal water [2]. SiO2 content in thermal

springs is relatively higher than the cold springs (72-101

mg/L). In the cations, Na is predominant (90-118 mg/L)

192 Geochemical Investigations on Thermal and Cold Springs at Dumka District,

Jharkhand, India



while concentration of K and Ca is low (<4 mg/L).

Thermal water contains very less amount of Mg (0.01-

0.3 mg/L). Thermal springs of the study area are Na-Cl

type; granites have probably played an important role in

providing the chloride to granite hosted geothermal

system [15, 16, 17, 18]. As seen from the Piper diagram,

the chemistry of thermal springs is compatible with the

chemistry of the host rock through which they circulate.

Cold springs of the area have surface temperature

between 25° to 27°C and are near neutral in the nature

(pH = 7.4-7.5). Cold springs fall in the Ca-HCO3 field

which suggests that the circulation of the cold springs is

within the alluvium or sedimentary formations.

Fig2: Piper trilinear diagram (1944) showing the geochemical variation of different water type from Dumka area.

4.2. Anion variation:

Water samples from the Dumka area were plotted in Cl-

SO4-HCO3 diagram [20], to select suitable samples for

estimation of reservoir temperature using cations

geothermometers. From Fig. 3 it is seen that the thermal

waters of Tantaloi area (sample # 7, 8) are falling in the

Cl field and the ratio of HCO3/Cl is less than unity.

Therefore, these thermal waters are believed to be fast

ascending with mild or no mixing with the near-surface

groundwater. Shifting of sample # 4 toward the SO4

field suggests mixing of volcanic gases with the thermal

waters. Other thermal springs falling in the HCO3 field

suggest that there is mixing of the near surface

groundwater.

Fig3: Cl-SO4-HCO3 diagram showing the position of water samples from Dumka area, symbols are similar as Fig2.

193 HEMANT K. SINGH, D. CHANDRASEKHARAM, TRUPTI G AND B. SINGH



4.3. Geothermometry:

4.3.1. Silica geothermometry:

Fournier [21] suggested a geothermometer to estimate

reservoir temperature based on the silica concentration

in thermal springs. Equations to calculate the reservoir

temperature are as follows:

Silica geothermometers with no steam loss:

Silica geothermometers with maximum steam loss:

Where, S is the concentration of silica in thermal fluid.

4.3.2. Cation geothermometry:

There are several cation geothermometers available;

some of them are used for the estimation of reservoir

temperature (Table 2).

Using silica geothermometers the estimated reservoir

temperature shows range of 117° to 138°C while cation

geothermometers suggests the reservoir temperature

range of 92° to 151 °C.

Giggenbach [20] proposed Na-K-√Mg ternary diagram

(Fig. 4) which indicates that most of the thermal water

samples fall in the partial equilibrium field, only

samples # 3 and 6 is trending towards the Mg corner i.e.

in the field of immature water zone, which could be due

to near surface groundwater mixing. With the help of

Fig. 4 estimated reservoir temperature ranges between

85° to 120°C. These reservoir temperature ranges

conclude that Dumka thermal springs can be classified

as low enthalpy geothermal system.

Table2: Estimated reservoir temperature of Dumka

thermal springs, based on chemical geothermometers

S. No.

Silica Cation

No

steam loss

Maximum

steam loss

Na-K

[22]

Na-K

[23]

Na-K-Ca

[24]

1 120.0 118.1 92.6 113.5 104.5

2 119.3 117.5 87.6 108.7 101.4

3 134.6 130.5 121.3 141.5 121.1

4 118.6 116.9 98.3 119.1 110.1

6 130.5 127.1 130.6 150.5 132.6

7 136.4 132.1 111.5 132.0 120.1

8 137.9 133.3 92.2 113.2 104.0

Fig4: Na-K-√Mg geothermometers ternary diagram of Giggenbach [20] showing the variation in Dumaka area

thermal springs.

5. Conclusions:

There are many thermal and cold springs located in the

Dumka district of Jharkhand, India. Surface temperature

of these thermal springs are in the range of 42° to 70 °C

while the cold springs temperature ranges from 24° to

27 °C. Thermal springs of the area are near neutral to

moderately alkaline in nature and are Na-Cl type as

observed from the Piper diagram. Position of the

thermal waters in Piper diagram also suggests that the

chemistry of the thermal water is compatible with the

host rock chemistry of the area. Cold springs of the area

are near neutral in nature and plot in the Ca-HCO3 field,

which suggests the circulation of cold springs may be

through the alluvium or sedimentary formations. Anion

variation diagram suggests that only sample # 7 & 8 are

fast ascending thermal fluid which show no or mild

mixing of near surface groundwater, whereas other

thermal water falls in the HCO3 field indicating near

surface groundwater mixing. Based on chemical

geothermometry, the average estimated reservoir

194 Geochemical Investigations on Thermal and Cold Springs at Dumka District,

Jharkhand, India



temperature ranges between 92° to 138 °C; which

suggests that Dumka geothermal field can be classified

as low enthalpy geothermal system.

6. Acknowledgements:

Authors are extremely thankful to Prof. N.J. Pawar and

Prof. Elango Lakshmanan for their valuable comments

and suggestions to improve the quality of the paper. We

thank Prof. D.V Reddy, Editor in Chief, IJEE for

encouraging publishing this paper in IJEE. We are also

very thankful to Department of Earth Science, IIT

Bombay for providing the facilities to carry out this

work.

7. Reference:

[1] Nagar, R.K., Vishwanathan, G., Sagar, S.,

Sankaranarayanan, A., Geological, geophysical and

geochemical investigations in Bakreswar-Tantloi

thermal field, Birbhum and Santhal Parganas

districts, West Bengal and Bihar, India. Proc. Sem.

on Geothermal Energy in India. In: Pitale, U.L.,

Padhi, R.N. Eds.., Geol. Surv. India-Spec. Pub. 45,

349–360, 1996.

[2] Ghose, D., Chowdhury, D.P., Sinha, B., Large-

scale helium escape from earth surface around

Bakreswar-Tantloi geothermal area in Birbhum

district, West Bengal, and Dumka district,

Jharkhand, India. Current Science. 82 (8), 993-996,

2002.

[3] ONGC (Oil and Natural Gas Commission),

Tectonic map of India. Oil and Natural Gas

Commission, Dehradun, India, scale 1:2000000,

1969.

[4] Sarkar, A.N., Precambrian tectonic evolution of

eastern India: a model of converging microplates.

Tectonophysics. 86, 363-397, 1982.

[5] GSI (Geological Survey of India), Geothermal

Atlas of India. Geological Survey of India Special

Publication: 19, 144, 1991.

[6] Shanker, R., Thermal and crustal structure of

“SONATA”. A zone of mid continental rifting in

Indian Shield. J. Geol. Soc. India 37, 211–220,

1991.

[7] Majumdar, N., Mukherjee, A.L., Majumdar, R.K.,

Mixing hydrology and chemical equilibria in

Bakreswar geothermal area, Eastern India. Journal

of Volcanology and Geothermal Research.183,

201-212, 2009.

[8] Dunn, J.A., Post Mesozoic movements in the

northern part of the Peninsular India. Mem. Geol.

Surv. India 73, 137–142, 1939.

[9] Ghosh, P.K., Mineral springs of India. Proceeding

35th International Science Congerace, Part 2, 221-

250, 1948.

[10] Desikachar, S.V., Himalayan orogeny and plate

tectonics-a geological interpretation. Misc. Publ.,

Geol. Surv. India. 34, 29–39, Part 1, 1974.

[11] Mahadevan, T.M., Geology of Bihar & Jharkhand.

Geological Society of India Bangalore. 1-563,

2002.

[12] Arnorsson, S., Isotopic and chemical techniques in

geothermal exploration, development and use.

IAEA, 2000.

[13] Marini, L., Geochemical techniques for the

exploration and exploration of geothermal energy.

Universita degli Studi di Genova, Italia, 2010.

[14] APHA, Standard methods for examination of water

and waste water, American Public Health

Association, 1977.

[15] Savage, D., Mark, M., The origin of saline

groundwater in granitic rocks: Evidence from

hydrothermal experiments. Materials Research

Society Symposium Proceeding. 50, 1985.

[16] Savage, D., Mark, R.C., Antoni E. Milodowski, Ian

George, Hydrothermal alteration of granite by

meteoric fluid: an example for the Carnmen ellies

granite, United Kingdom. Contrib. Mineral. Petrol.

96: 391–405, 1987.

[17] Chandrasekharam, D., Antu, M.C., Geochemistry

of tattapani thermal springs, Madhya Pradesh,

India-field and experimental investigations.

Geothermics. 24(4): 553–559, 1995.

[18] Singh, H.K., and Chandrasekharam, D., Evaluation

of Tuwa geothermal system through water-rock

interaction experiment. Water-Rock Interaction-13,

Taylor and Francis Group, London. 181-183, 2010.

[19] Piper, M., A graphic procedure in the geochemical

interpretation of water-analyses. American

Geophysical Union 25, 914-923, 1944.

[20] Giggenbach, W.F., Geothermal solute equilibria.

Derivation of Na-K-Mg-Ca geoindicators.

Geochica et Cosmochimica Acta. 52, 2749-2765,

1988.

[21] Fournier, R.O., Silica in thermal waters: Laboratory

and field investigations. Proceedings International

Symposium on hydrogeochemistry and

biogeochemistry, Tokyo, 1, 122–139, 1973.

[22] Fournier, R.O., A method of calculating quartz

solubilities in aqueous sodium chloride solution.

Geochimica et Cosmochimica Acta. 47, 579-586,

1983.

[23] Giggenbach, W.F., Gonfiantini, R., Jangi, B.L.,

Truesdell, A.H., Isotopic and chemical composition

of Parbati Valley geothermal discharges, NW-

Himalaya, India. Geothermics. 12, 199-222, 1983.

[24] Fournier, R.O., Truesdell, A.H., An empirical Na-

K-Ca geothermometer for natural water.

Geochimica et Cosmochimica Acta. 37, 1255-1275,

1973.

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ISSN 0974-5904, Volume 07, No. 01

February 2014, P.P.195-202


Prediction of Daily Pan Evaporation Using Support Vector

Machines

LEELADHAR PAMMAR1 AND PARESH CHANDRA DEKA

2

1N.M.A.M Institute of Technology, NITTE, Karnataka, INDIA

2Department of Applied Mechanics and Hydraulics, National Institute of Technology Karnataka, INDIA

Email: [email protected], [email protected].

Abstract: Water scarcity globally has lead to severe problems in water management. Understanding the rate of

evaporation, from surface water resources is essential for precise management of the water balance. However,

evaporation is difficult to measure experimentally due to its nature. Preparing reliable forecasts of evaporation has

become an essential element towards efficient water management. The objective of this paper is to predict daily pan

evaporation using different kernel functions of Support Vector Machines (SVM’s) based regression approach for the

meteorological data obtained for the region ‘Lake Abaya’ which is located in the Great Rift Valley, southern part of

Ethiopia. The meteorological parameters considered for study includes daily details of mean-temperature (T), wind

speed (W), sunshine hours (Sh), relative humidity (Rh), rainfall (P). Among the kernel functions used for study, the

polynomial kernel function proved its credibility by showing improved performance in training and testing periods.

The evidence for performance of polynomial kernel function was seen in terms of correlation coefficient (CC)

obtained for training and testing is respectively 0.940, 0.956 which is acceptable.

Keywords: Evaporation, Support vector machine, Kernel functions.

1. Introduction:

Evaporation losses create biggest impact in water

management. Water managers should be aware in

advance to avoid crisis. In water scarce areas,

evaporation losses become prime factors of the water

budget for a lake or reservoir, and may affect

significantly in lowering of the water surface elevation

[1]. Water managers are finding new ways of reducing

in-efficiencies in water supply systems, including the

evaporative loss of water from reservoirs. Because of its

nature, evaporation from water surfaces is rarely

measured directly, except over relatively small spatial

and temporal scales [2]. The use of pans of water for

measuring evaporation routes back to the 18th century.

It is easy to understand their intuitive appeal as they

measure open water evaporation in a visible way. The

pan evaporation is widely used method of estimating

evaporation from lakes and reservoirs [12]. However,

despite numerous studies, it is very difficult to use data

from pans except in specific circumstances.

There are many methods available that estimate

evaporation from an open water body, also known as

lake evaporation. Methods include the water budget

method, energy budget method, eddy correlation

method, mass-transfer approach, the Penman method,

combination equation and the pan coefficient method

[3].Numerous researchers have attempted to estimate

the evaporation values from climatic variables, and most

of these methods require data that are not easily

available. The indirect methods, in increasing order of

complexity and data requirements, include temperature-

based formulas [13]; radiation-based approximations

[14]; humidity-based formulas [15]; combination

formulae, which include allowance for humidity and

wind speed [16]; or even more intensive evaluations of

an energy balance at the evaporation surface [17].

Studies made on data driven models reveals the need for

suitable approach to model and should have the ability

to take care of non-linear behaviour of the system [11].

Artificial neural network based modeling technique has

been used to study the influence of different

combinations of meteorological parameters on

evaporation from a reservoir. The comparison

demonstrated superior performance of artificial neural

network over linear regression approach. The findings

of the study also revealed the requirement of all input

parameters considered together, instead of individual

parameters taken one at a time as reported in earlier

studies, in predicting the evaporation. [4].

Estimation of evaporation carried with ANFIS

(Adaptive Neuro Fuzzy Inference System) approach

performed successfully in modeling the evaporation

process than fuzzy sets [5].



196 Prediction of Daily Pan Evaporation Using Support Vector Machines



Previous works on data modeling suggests that ANN

and ANFIS techniques have good performances for the

test data set; Between ANN and ANFIS, ANFIS model

is slightly better albeit the difference is small. [10].

Recently, the SVM method has find applications in

various areas of hydrology: SVM was succefully

implemented for predicting floods [19]. Literature also

shows use of SVM method for identifying the structure

of a radial function in networks. One of the papers

includes the modeling between rainfall and river

discharges using the SVRBFN [20]. SVM also proposed

on prediction of daily runoff combining with Chaos

Theory [21].

The SVM algorithms have been widely used in various

modeling works including evaporation, the topic under

consideration of present work. The SVMs algorithm

provides good estimation of evaporation. The work

conducted on modeling evaporation using SVM

algorithm shows better performance in comparison to

Multiple Linear Regression (MLR) [27]. The findings of

the study conducted on estimating the pan evaporation

from reservoirs suggests the usefulness of support

vector machines algorithm technique [6]. SVMs

technique used for simulating evaporation, results

reveals better performance of SVM; the authors also

tried Gamma Test (GT) for the first time in modeling

one of the key hydrological components i.e evaporation

[26].

Support vector machines are classification or regression

methods, which have been derived from statistical

learning theory [7]. SVM’s are good at producing

accurate and robust classification results on a sound

theoretical basis, even when input data are non-

monotone and non-linearly separable [7]. So they can

help to evaluate more relevant information in a

convenient way. The accuracy of results does not

depend on the quality of human expertise judgment for

the optimal choice of the linearization function of non-

linear input data, since they linearize data on an implicit

basis by means of kernel transformation [7]. SVM’s

operate locally, so they are able to reflect in their score

the features of single companies, comparing their input

variables with the ones of companies in the training

sample showing similar constellations of financial

ratios. Although SVM’s do not deliver a parametric

score function, its local linear approximation can offer

an important support for recognizing the mechanisms

linking different financial ratios with the final score of a

company [7]. For these reasons SVM’s are regarded as a

useful tool for effectively complementing the

information gained from classical linear classification

techniques.

Recent literatures showed that SVM’s provide a

promising alternative to conventional artificial neural

networks for statistical downscaling [7]. SVM method

was also applied for a one-day prediction of rainfall and

runoff. The data input of the model was acquired by

singular spectrum analysis and included a large entrance

space [22]. SVM is also utilized for the classification of

remote sensing data, which was later used for modeling

between rainfall and runoff and comparing the method

with artificial neural networks, SVM achieved good

results for prediction. [23]. Some authors also examined

the capabilities of SVM to devise optimum monitoring

networks for groundwater and concluded that SVM can

be used as an optimum method for selecting the optimal

stability network [24]. In one of the paper on SVM, it is

also seen authors categorized the span of river flow into

three parts and used the SVM method to predict the

daily flow in these three regions. [25].

The SVM tools have wide range of kernel functions

with various parameters, which helps users to generate

appropriate classification or regression. Three well

known kernels were employed to study the

performance.

with the complexity parameter (C) = 1, exponent (E) =

1, epsilon parameter - The epsilon parameter of the

epsilon insensitive loss function = 0.001 and normalized

data filter.

with the complexity parameter (C) = 1, exponent (E) =

1, epsilon parameter - The epsilon parameter of the

epsilon insensitive loss function = 0.001 and normalized

data filter.

with the complexity parameter (C) = 1, gamma value =

0.01, epsilon parameter = 0.001 and normalized data

filter.

Literature on parameter optimization suggests that with

proposed choice of E, the value of complexity

parameter C has only negligible effect on the

generalization performance. If C is too large, then the

classification accuracy rate is very high in the training

stage, but very low in the testing stage. If C is too small,

then the classification accuracy rate is unsatisfactory,

making the model useless. Parameter C has negligible

influence on classification outcomes, because its value

influences the partitioning outcome in the feature space.

An optimal choice of the loss function i.e. epsilon

parameter (E) should match a particular type of noise

density [28].

For this study, polynomial kernel a value of C =1.0

showed better results.

197 LEELADHAR PAMMAR AND PARESH CHANDRA DEKA



In this paper it is attempted to compare the performance

of SVM’s among its three well known kernel functions

in predicting evaporation and compared results to

validate the models.

2. Support vector machines basics:

Support vector regression (SVR) is used to describe

regression with SVM’s in the open literature. In

regression estimation with SVR, attempt is made to

estimate a functional dependency f x between a set

of sampled points 1 2, ,......., lX x x x taken from nR and

target values 1 2, ,........, lY y y y with iy R (Herein,

the input and target vectors ( 'x s and 'y s ) refer to the

pan evaporation, predicted evaporation). Let us assume

that these samples have been generated independently

from an unknown probability distribution function

and a class of functions: [7].

, : ,n nF f f x w x B w R R R

(1)

Where w and B are coefficients that have to be

estimated from the input data. Herein, the fundamental

problem is to find a function f x F that minimizes a

risk functional:

, ,R f x l y f x x dP x y (2)

Where ‘l’ is a loss function used to measure the

deviation between the target, y, and estimate, f x

values. As the probability distribution function ,P x y

is unknown one cannot minimize R f x directly but

only compute the empirical risk function as:

1

1 n

emp i i

i

R f x l y f xN

(3)

This traditional empirical risk minimization is not

advisable without any means of structural control or

regularization. Therefore a regularized risk function

with the smallest steepness among the functions that

minimize the empirical risk function could be used as:

2

reg empR f x R f x w (4)

Where is a constant ( 0 ). This additional term

reduces the model space and thereby controls the

complexity of the solution. For this reason, the

following form of this expression can be considered [8];

[10]:

21

(2

i

reg C i i

x X

R f x C l y f x w

(5)

Where Cc is a positive constant (i.e. additional capacity

control parameter) that has to be chosen beforehand.

The constant Cc that influences a trade-off between an

approximation error and the regression (weight) vector

w is a design parameter. The loss function in this

expression, which is called Ɛ-insensitive loss function,

has the advantage that ther is no need of all the input

data for describing the regression vector w and can be

written as:

i il y f x (6)

This function behaves as a biased estimator when

combined with a regularization term 2w . The loss

is equal to 0 if the difference between the

predicted if x and the measured value iy is less than

Ɛ. The choice of Ɛ value is easier than the choice of Cc

and it is often given as desired percentage of the output

values iy Hence, nonlinear regression function n is

given by function that minimizes Eq. (5) subject to Eq.

(6) as in the following expression [8];[11];[10]:

1

,N

i i i

i

f x x x B

(7)

Where , 0i i is the Lagrange multipliers, B is a

bias term, and , ix x is the Kernel function which is

based upon Reproducing Kernel Hilbert Spaces. The

data are often assumed to have zero mean (this can be

achieved by pre-processing), so the bias term is

dropped. The kernel function is to enable operations to

be performed in the input space rather than the

potentially high dimensional feature space. Hence an

inner product in the feature space has an equivalent

kernel in input space. In general, the Kernel functions

treated by the SVR are the functions with the

polynomial, RBF, Gaussian Radial Basis, Exponential

Radial Basis etc.

2.1. Study area:

Lake Abaya is located in the Great Rift Valley in the

southern part of Ethiopia. The map showing the study

area is displayed in figure 1. Lake Abaya is

approximately 60 km long and 20 km wide with a

surface area of 1162 sq.km with an average depth of

7.1m, located around latitude and longitude 6°26′N &

37°53′E respectively with maximum depth 13.1 m and

is at an elevation of 1285 m from mean sea level.




There are number of small islands in the lake with water

volume is about 8.2 cubic km. The geology of the lake

is of 50% volcanic origin with equally sedimentary and

crystalline strata. The quality of the lake is alkaline-

saline with dominant ions being bicarbonate, sodium

and chloride. It may be due to longer residence time,

low freshwater inputs with high evaporation rate. The

lake is red due to high load of suspended sediment.

Mean monthly evaporation is around 150 mm. [29].

Fig1: The Abaya lake Rift Valley drainage region

Lake Abaya does not always have an outflow, but in

some years it overflows into Lake Chamo which is few

km away to the south. The existing irrigation area

covered by the lake is not too much significant. The

mean annual rainfall is around 1000 mm with

temperature changes from 24 °c to 30 °c throughout the

year as no significant wind speed variation.

2.2. Modeling evaporation with SVM:

Model selection and parameters selection decides the

performance of SVM models. However in general there

is no guidance for kernel function selection. It depends

upon the data input pattern. The parameters comprising

the data set include pan evaporation (E) as the output

attribute and five input attributes representing mean

temperature (T), wind speed (W), sunshine hours (Sh)

and relative humidity (Rh), rainfall (P) . Table 1

displays the statistical analysis of attributes considered

for the study.

For model building and validation a total of 300 data

points were used in the present study. Figure 2 shows

the variation of above listed attributes with respect to

time (meteorological parameters considered for 300

days in the year 2005) [29]. The influences of the

parameters on the class attribute i.e pan evaporation

decide the accuracy of prediction and discussion with

results is made in subsequent section of results and

discussion of this paper.

Table1: Statistical analysis of the weather data taken for 300 days (Year 2005).

S.

No. Attribute Xmax. Xmin.

Standard deviation

Sd.

Coefficient of variation

Cv.

1 Mean temperature (°c) 28.60 14.10 1.84 0.07

2 Wind speed (m/s) 15.99 0.39 2.22 0.32

3 Sunshine hours (No’s) 11.4 0.0 2.81 0.36

4 Relative humidity (%) 99 23 13.06 0.23

5 Rainfall (mm) 102.4 0.0 2.91 9.69

6 Pan evaporation (mm) 8.0 4.0 1.27 0.19

(a) (b)




(b) (d)

(e) (f)

Fig2: (a), (b), (c), (d), (e), (f) showing variation of attributes pan evaporation (E), temperature (T), wind speed (W),

sunshine hours (Sh) and relative humidity (Rh), and rainfall (P) with respect to time.

The Support Vector Machines is used to calculate

correlation coefficient and root mean square error

(RMSE) on training and testing models. The data set

was categorized as training and testing data with ratio of

70% training and 30% testing. Trained performance was

be re-evaluated upon test models. SMO-Reg i.e the

support vector machine for regression classifier

employed to generate the model on input data set in

predicting the pan evaporation. To reach at a suitable

choice of these parameters, the correlation coefficients

and Root Mean Square Error (RMSE) were compared

and the parameter combination providing smallest value

of RMSE and higher value of correlation coefficient

was selected for final results.

Root Mean Square Error: RMSE is a frequently used

measure of the differences between predicted and

observed values. The root mean square error is specially

suited to iterative algorithms and is a better measure for

high values. It offers a general picture of the errors

involved in prediction. The measures involving the

error-square terms are also sensitive to extreme values

[18].

Coefficient of correlation: It represents the linear

dependence between the two variables under

consideration. It is a popular global error statistic for

measuring the goodness of fit of the models and tends to

give higher weight for the large difference attributable

to the square of the difference between observed and

predicted inflows. It quantifies the efficiency of a model

in capturing the complex, dynamic and nonlinear nature

of the physical process being modeled. C.C equals to 1

indicates a perfect fit.

Training and Testing models: Out of 300 data points

210 were selected for training purposes and remaining

reserved for testing models with above mentioned

parameters.

The SVM kernel functions employed produces different

classification or regression of data, which in turn help

users to analyze and interpret the most influential

parameters relating to class attribute i.e evaporation.

2.3. Results and discussion:

Among the five meteorological variables considered, it

is possible that some may influence lot on the output




parameter than others. However it is observed fact in the

nature that the climatic or meteorological factors in

general act in concert. Therefore, it is relevant to take

into account the combined influence of all the

meteorological parameters on evaporation. In this study

a combination of temperature, wind speed, sunshine

hour, relative humidity, and rainfall tried which

provides a maximum value of correlation coefficient

with minimum values of root mean square error in

comparison to other inputs combinations.

In order to exhibit a fair comparison of the SVM’s

approach well known kernel functions were tested in

terms of the correlation coefficient and root mean

square errors. The results obtained were better as well as

comparable to cross validation.

The cross-validation is a method of estimating the

accuracy of a classification or regression model in

which the input data set is divided into several parts (a

number defined by the user), with each part in turn used

to test a model fitted to the remaining parts. Usually 10-

fold cross validation is followed.

In total three SVM kernel functions were selected to

demonstrate their performance in predicting reliable and

accurate results. Results are displayed in table 2.

Table2: Results of models

Sl.

No. Kernel or function employed

Training Testing

CC RMSE CC RMSE

1 Polynomial 0.940 0.449 0.956 0.374

2 Normalised Polynomial 0.953 0.395 0.952 0.768

3 Radial basis function 0.863 0.764 0.834 1.156

As mentioned earlier kernel functions provides different

regression methods. The adopted regression must avoid

errors of over-fitting and under-fitting. From the above

listed results, it is clear that the few kernel functions

performs better in training period but fails to produce

better or similar results in testing. It is also seen few

maintains the rate of prediction near constant in both

training and testing periods. However it is pertinent to

arrive at better solution. Among the listed kernel

functions, it is clear that polynomial kernel function

perform well with the combination of inputs formed

both in training and testing periods than remaining. It is

also seen that among the SVM kernel functions

employed polynomial and normalized polynomial

functions performing nearly in the same way, but the

latter fails to maintain consistency.

As per result polynomial kernel showed better

performance than other two kernel functions. Since the

results are more or less similar between polynomial and

normalized polynomial, for interpretation purpose two

kernels; one with constant performance and another

with lowest performance selected. Following figures

highlights the performance of polynomial kernel in

comparison to Radial basis function. Further

conclusions can be made on the plots.

Fig3: Prediction values of pan evaporation for training

and testing models with the polynomial kernel.

Fig4: Prediction values of pan evaporation with the

radial basis function.




Out of the six meteorological variables considered, it is

clear that some would play very prominent role in

deciding the prediction accuracy. However it is equally

important to consider them as a unit to analyze their

combined effect. Figure 3 and figure 4 distinguishes the

performance of kernel accuracy in predicting pan

evaporation.

Figure 5 highlights the correlation among prediction and

actual pan evaporation values tested with polynomial

kernel.

Fig5: Scatter plot between actual and predicted values

with polynomial kernel function.

Figure 6 indicates performance indexes comparison of

results with two different kernels both training and

testing periods.

Fig6: Comparison of kernel performance functions

using statistical parameters.

2.4. Conclusions:

The various meteorological parameters influences on

the classifying attribute (i.e evaporation) in their own

way. However it is the combined effect which decides

rate of evaporation. SVM kernel functions, produces

different regression of data. Those results can be

suitably interpreted and used to arrive at reliable and

accurate solutions. Comparison of CC and RMSE

suggests an improved and constant performance of

polynomial kernel of SVM both in training and testing

periods. The factors for such performance may be

attributed to several user defined parameters

implemented in SVM. The SVM tools offers less

computational time in displaying results. The results

encourages SVM’s based modeling technique in

accurate estimation of the evaporation as well as help to

overcome drawbacks faced in approaches as proposed

in previous studies. There is a wide scope for further

kernel functions to explore in forecasting large time

series data.


The authors are grateful to Dr. Mekonen Ayana, dean,

school of post graduate studies, Arbaminch University,

Ethiopia for his valuable support and access to data for

the research work.

The author wishes to thank reviewers for their

constructive comments to improve the article and editor

in chief for continuous communication and support.

4. Reference:

[1] R. H. McCuen, Hydrologic Analysis and Design,

2nd edition, Prentice Hall, Upper Saddle River,

New Jersey, 1998.

[2] F. E. Jones, Evaporation of Water: With Emphasis

on Applications and Measurements, Lewis,

Chelsea, Michigan, PP 188, 1992.

[3] S. L. Dingman, Physical Hydrology, Volume. 14.

Prentice Hall, Upper Saddle River, PP. 575, 1994.

[4] S. Deswal, and M. Pal, “Artificial Neural Network

based Modeling of Evaporation Losses in

Reservoirs”, World Academy of Science,

Engineering and Technology, Volume No. 39, PP.

279-283 2008.

[5] E. M. Keskin, O. Terzi, D. Taylan, “Estimating

daily pan evaporation using adaptive neural-based

fuzzy inference system”, Theor Appl Climatol,

Issue No. 98, PP. 79–87, 2009.

[6] S.Tripathi, V. V. Srinivas, S. R. Nanjundiah,

“Downscaling of precipitation for climate change

scenarios: A support vector machine approach”,

Journal of Hydrology. Volume No. 330, Issue No.

3-4, PP. 621– 640, 2006.

[7] V. N. Vapnik, the Nature of Statistical Learning

Theory, New York, Springer Verlag, 1995.

[8] M. Cimen, “Estimation of daily suspended

sediments using support vector machines”,

Hydrological Sciences Journal, Volume No. 53,

Issue No. 3, 2008.

[9] S. Gunn, “Support Vector Machines for

Classification and Regression”, Image Speech &

Intelligent Systems Group, University of

Southampton, United Kingdom, 1998.




[10] P. Kumar, D. Kumar, Jaipaul, A. K. Tiwari,

“Evaporation Estimation Using Artificial Neural

Networks and Adaptive Neuro-Fuzzy Inference

System Techniques” Pakistan Journal of

Meteorology, Volume. 8, Issue. 16, 2012.

[11] K. S. Kasiviswanathan, R. S. R. Pandian, S.

Saravanan, A. Agarwal, “Genetic programming

approach on evaporation losses and its effect on

climate change for vaipur basin”, IJCSI

International Journal of Computer Science Issues,

Volume No.8, Issue No.5, 2011.

[12] S. S. Eslamian and M. Feizi, maximum monthly

rainfall analysis using L-moments for an arid region

in Isfahan province”, Iran. J. Applied Meteorol

climatol, Volume No. 46, Issue 4, PP.495-503,

2007.

[13] C. W. Thornthwaite, “Na approach toward a

rational classification of climate”, Geographical

Reviews, Volume No. 38, Issue No.1, PP. 55-94.

1948.

[14] L. Turc, “Estimation of irrigation water

requirements, potential evapotranspiration: A

simple climatic formula evolved up to date”, Ann.

Agron., Issue No.12, PP 13–49, 1961.

[15] V. A. Romanenko, “Computation of the autumn

soil moisture using a universal relationship for a

large area”, Proc. Ukrainian Hydrometeorological

Research Institute, No. 3, Kiev, 1961.

[16] H. L. Penman, “Natural evaporation from open

water, bare soil, and grass”, Proc. R. Soc. London,

Issue No. 193, PP. 120–145, 1948.

[17] R. S. McKenzie and A. R. Craig, “Evaluation of

river losses from the Orange River using hydraulic

modeling.” J. Hydrol., Volume No.1, Issue No.241,

PP. 62–69, 2001.

[18] N. Karunanithi, W. J. Grenney, D. Whitley, and

Bovee, K., “Neural networks for river flow

prediction”, J. Comput. Civ. Eng. , Volume No. 8

Issue No. 2, PP. 201–220, 1994.

[19] S. Y. Liong, & C. Sivapragasm, “Flood stage

forecasting with SVM”, J. Am. Water Res. Assoc.

Volume No. 38, Issue No.1, PP. 173–186, 2002.

[20] K. Y Choy, C. W. Chan, “Modelling of river

discharges and rainfall using radial basis function

networks based on support vector regression”,

International Journal of Systems Science, Volume

No. 34 Issue No. 14–15, PP. 763–773, 2003.

[21] X. Y. Yu, S. Y. Liong, V. Babovic, “EC-SVM

approach for realtime hydrologic forecasting”, J.

Hydroinf. Volume No. 6 Issue No.3, PP. 209–233,

2004.

[22] C. Sivapragasam, S. Y. Liong, & M. F. K. Pasha,

“Rainfall and discharge forecasting with SSA-SVM

approach” J. Hydroinformatics, Volume No. 3,

Issue No. 7, PP. 141–152, 2001.

[23] Y. B. Dibike, S. Velickov, D. P. Solomatine and M.

B. Abbott, Model Induction with Support Vector

Machines: Introduction and Application, Journal of

Computing in Civil Engineering, ASCE, Volume

No. 15 Issue No. 3, PP. 208-216, 2001.

[24] T. Asefa, M. W. Kemblowski, M. McKee, and A.

Khalil Multi-time scale stream flow prediction: The

support vector machines approach. Journal of

Hydrology, 318, PP. 7–16, 2006.

[25] C. Sivapragasam, S. Y. Liong, “Flow categorization

model for improving forecasting”, Nordic

Hydrology, Volume No. 36, Issue No. 1, PP. 37–

48, 2005.

[26] A. Moghaddamnia, M. Ghafari, J. Piri, and D. Han,

“Evaporation Estimation Using Support Vector

Machines Technique”, World Academy of Science,

Engineering and Technology, Volume No. 19, PP.

14-22, 2008.

[27] S. Deswal & Mahesh Pal “Modeling of Pan

Evaporation Using Support Vector Machines

ALGORITHM”, ISH Journal of Hydraulic

Engineering, Volume No.14, Issue No.1, PP. 104-

116, 2012.

[28] V. Cherkassky and Y. Ma, “Practical Selection of

SVM Parameters and Noise Estimation for SVM

Regression” Neural Networks, Volume No.17,

Issue No.1, PP 113–126, 2004.

[29] S. B. Awulachew, “Investigation of physical and

bathymetric characteristics of lakes Abaya and

Chamo, Ehiopia & their management implications”,

lakes and reservoirs: research & management,

Volume No. 11, Issue No.3, PP. 133-140, 2006.

www.cafetinnova.org

Indexed in





ISSN 0974-5904, Volume 07, No. 01

February 2014, P.P.203-212


Fluoride Distribution in the Groundwater of Narsampet Area,

Warangal District, Andhra Pradesh, India

V. SUDARSHAN1, S. GEETA

2, A. NARSIMHA

1, S. SHANKAR

1 AND A. RAVI KUMAR

1

1Department of Applied Geochemistry, University College of Science, Osmania University, Hyderabad – 500 007,

India 2Department of Chemistry, MVSR Engineering College, Hyderabad – 501 510, India

Email: [email protected], [email protected], [email protected],

[email protected], [email protected]

Abstract: In order to assess the Fluoride contamination in the groundwater of Narsampet area of Warangal district

of Andhra Pradesh, the study was conducted in the months of January 2012, November 2012 and July 2013. The

Fluoride concentration along with EC, pH in groundwater samples was determined in various villages of Narsampet

area. It is observed that the pH of groundwater in all the three seasons was well within limits and groundwater was

alkaline in nature. Electrical conductivity of the groundwater at 25oC varies from 92.3 to 5220 µS/cm (average 2118

µS/cm) during Jan 2012 post monsoon, 515 to 5974 µS/cm (average 1851 µS/cm) in the Nov 2012 post monsoon

and 392 to 9072 µS/cm (average 2129 µS/ cm) during July 2013 pre-monsoon season and Fluoride concentration

in the groundwater varies from 0.2 to 8 mg/L in January 2012, 0.3 mg/L to 8.0 mg/L in November 2012 post

monsoon and 0.47 mg/L to 5.1 mg/L in July 2013 pre monsoon seasons. While 35.1% of groundwater shows excess

fluoride prescribed for drinking purpose in January 2012 post monsoon, 46.8% of the groundwater contains excess

fluoride in the November 2012 post monsoon and 37.5% of the ground water contains excess fluoride in July 2013

pre monsoon seasons.

Keywords: Fluoride, fluoride contamination, Warangal district, Andhra Pradesh, India.

1. Introduction:

Fluoride is one of the very few chemicals that cause

significant influences on human health through drinking

water [1]. The optimal concentration of fluoride in

drinking water varies according to climatic conditions;

the range of 0.5-1.5 mg/L is generally recommended by

WHO [2]. Fluoride contributes to dental health and to

the maintenance of appropriate bone density. Fluorine is

the lightest halogen and also the most electronegative

element, which indicates its strong tendency to acquire a

negative charge and form F¯ ions in solution [3]. Due to

its high reactivity Fluorine is found as fluoride in the

environment, which together represent about 0.06–0.09

% of the earth’s crust. Fluoride occurs naturally in rock,

soil, water, plants, and animals [4] [5]. Groundwater

gets contaminated due to various geogenic and

anthropogenic activities. Fluoride (F-) concentration is

an important aspect of hydro geochemistry, because of

its impact on human health.

The problem of high concentration of fluoride in

groundwater resources has become one of the most

important toxicological and geo-environmental issues in

India. In most of the fluorosis endemic areas, the

average summer temperature is above 27.5oC and

average drinking water consumption is more than 4

liters per day [6]. In India, about 62 million people,

including 6 million children, suffer from fluorosis due

to the high content of F- in water [7]. Most parts of

Andhra Pradesh in India have highly endemic fluorosis

zones [8, 9, 10, 11, 12, 13, 14 and 15]. The first case of

endemic fluorosis in India was reported as long ago as

1937 in Podili, Darsi and Kanigiri taluks of Prakasam

district, Andhra Pradesh [16 and 17]. The fluoride is

beneficial to certain extent when present in the

concentration of 0.8 to 1.0 mg/L for classification of

dental enamel especially for children below 8 years

[18], whereas causes dental fluorosis if present in excess

of 1.5 mg/L and skeletal fluorosis beyond 3.0 mg/L if

such water is consumed for 6 months to several years

[19].

Most F- accumulation in the human body occurs through

F- contaminated drinking water, substantial amounts of

F- can also be ingested through crops and vegetables

irrigated with F- contaminated water [20]. In the present

paper, occurrence of fluoride in parts of Narsampet area,

Warangal district is highlighted.

1.1 Study area:

mailto:[email protected],%[email protected]


204 Fluoride Distribution in the Groundwater of Narsampet Area, Warangal District,




The present study area is located in the central east part

of Warangal district of Andhra Pradesh and forms a part

of the Survey of India toposheet 56 O/13 (Figure 1).

The area geographically lies between longitude 79° 32"

– 79° 54" East and latitude 17° 33" – 17° 55" North.

The study area goes through a hot climate during

summer (April-May) with a temperature range 30-46°C

and in winter 12-29°C. The average annual rainfall is

1114 mm occurring during monsoon (June-September).

The area is located at a distance of 176 km from

Hyderabad. The area is occupied by the Granitic rocks

of Archaean age.

Fig1: Location of the Study Area

2. Materials and Methods:

The sampling locations were fixed by Global positioning

system (GPS). Groundwater samples were collected

from regularly used bore wells, hand pumps and open

wells location map of the groundwater samples is

presented in Figure 2. Fifty seven, forty seven and fifty

six samples are collected during post monsoon (January

2012) and (November 2012), pre-monsoon season (July

2013) in Narsampet and Chennaraopet areas of

Warangal district. The samples were collected in clean

two liter polythene bottles and analyzed for pH,

electrical conductivity (Ec) and fluoride (F-) as per

standard methods [21]. The pH and conductivity were

measured with pH meter and (Systronic) conductivity

meter (CM-180). Fluoride concentrations were

measured with Orion ion analyzer. The analytical

results are presented in the Tables 1, 1a, 2, 2a and 3,

3a.

3. Results and discussion:

3.1. pH:

The pH of the groundwater is varying between 7.33-

8.55 and 7.45 - 8.62 for post and 7.36 - 8.51 for pre-

monsoon seasons respectively. Groundwater in both the

seasons is alkaline (pH more than 7) in nature. There is

no general trend in the pH distribution (Figure. 3a, 4b

and 5a). pH value in all the three seasons remained

constant. pH is well within permissible limit (6.5 to

8.5).

3.2. Ec:

Electrical conductivity of the groundwater varies from

92.3 to 5220 µS/cm at 25oC (average 2118 µS/cm) in

the post monsoon, (January 2012) (Figure 3b) and 515 to

5974 µS/cm (average 1851 µS/cm) during post monsoon

season, (November 2012) (Figure 4a). In pre-monsoon

(July 2013) (Figure 5b) the range of EC is 392 µS/cm to

9072 µS/cm (average 2129 µS/cm). The acceptable

limit of Ec in drinking water is less than 1500 µS/cm

[21]. 61.4% and 48.9% of samples in post monsoon and

60.71% of samples in pre monsoon show values

higher than the prescribed limit. The higher values of

electrical conductance are indicative of high ionic

concentrations in the groundwater.

3.3. Fluoride (F-):

Fluoride concentration in the groundwater varies from

0.2 mg/L to 8.0 mg/L and 0.3 mg/L to 8.0 mg/L in the

post monsoon seasons (January 2012 and November

2012) (Figure 3c and 4c) and 0.47 to 5.1 mg/L in pre-

monsoon season, July 2013 (Figure 5c). while 35.1 and

46.8% of groundwater shows excess fluoride prescribed

for drinking purpose in post monsoon (Jan 2012 and

Nov 2013) 37.5% of the groundwater contains excess

fluoride in the pre monsoon (July 2013)). Highest

permissible limit is 1.5 mg/L [21]. The maximum

concentration of fluoride is found to be 8.0 mg/L in

January 2012 and November 2012. (Mukdumpuram,

North West and Ayyappa swami temple, North central

part of the study area) (Figure 3c and 4c)

205 V. SUDARSHAN, S. GEETA, A. NARSIMHA, S. SHANKAR AND A. RAVI KUMAR



Fig2: Location of the groundwater samples collected in the study area in January 2012.

In January 2012 eight samples (Ayyappa swami temple,

Narsampet, Sarvapuram, Khanapur, Mukhudhumpuram,

Gurjala 1, Gurjala 2, and Marrinarasaiahpally) were

having concentration 3 mg/L or more whereas in

November and July the number is five (Ayyappaswami

Temple, Narsampet Sarvapuram1, Sarvapura 2,

Dwarakapet) and four (Ayyappaswami Temple,

Dwarakapet, Marrinarasaiahpally) respectively. In post

monsoon and pre-monsoon seasons fluoride

concentration was maximum in Ayappa swami temple,

Narsampet 8 and 5 mg/L. 4 to14% of the water samples

during three seasons were below the prescribed

concentration of 0.6 mg/L.





Fig3a: Shaded contour map of pH for Jan 2012

Fig3b: Shaded contour map of Ec for Jan 2012

Fig3c: Shaded contour map of F for Jan 2012




Fig4a: Shaded contour map of Ec for Nov 2012

Fig4b: Shaded contour map of pH for Nov 2012

Fig4c: .Shaded contour map of F for Nov 2012





Fig5a: Shaded contour map of pH for July 2013

Fig5b: Shaded contour map of Ec for July 2013

Fig5c: Shaded contour map of F for July 2013




Table1a: statistical parameters for January 2012

Parameter Min Max Mean Acceptable

limit

% of samples

exceeding limit

pH 7.33 8.55 7.88 7.0-8.5 1.75

Ec 92.3 5220 2118 1500 61.4

F 0.2 8 1.6 1.0-1.5 35.1

Table1: Results of the Chemical Analysis of Groundwater samples collected from Narsampet and Chennaraopet

Areas, Warangal District (January, 2012).

S. No pH EC F

-

S. No pH EC F

-

µS/cm mg/L µS/cm mg/L

1 7.7 1400 2 29 8.37 1010 0.7

2 7.61 2380 2 30 8.39 2820 8

3 8.42 92.3 0.2 31 7.86 3290 1

4 7.85 1190 1 32 7.85 3480 1

5 7.99 1970 0.5 33 7.71 3550 0.3

6 7.88 1360 0.4 34 8.02 1830 2

7 7.88 1830 1 35 7.99 1920 1

8 8.38 1180 6 36 7.86 1490 0.3

9 7.93 1220 3 37 7.33 3840 0.7

10 8.07 67 5 0.7 38 7.6 1950 1

11 8.14 328 0.6 39 8.17 1320 2

12 7.62 2440 1 40 7.84 1720 1

13 7.58 4490 2 41 7.99 1750 2

14 7.95 1060 0.8 42 7.92 3280 2

15 7.45 865 1 43 7.97 1640 0.9

16 7.68 1140 1 44 7.56 1980 2

17 7.75 2970 3 45 7.91 2690 2

18 8.19 810 2 46 7.89 2540 5

19 8.19 453 0.7 47 8.55 1220 7

20 7.79 2110 4 48 7.36 2670 1

21 8.26 1200 0.9 49 7.36 5220 0.2

22 7.95 1590 1 50 7.61 3330 0.9

23 7.55 3290 0.9 51 7.95 1470 0.8

24 7.95 1240 2 52 8.39 1580 3

25 7.75 1710 0.8 53 7.67 4820 1

26 7.35 4940 0.4 54 8.32 1340 2

27 7.7 1690 0.4 55 7.68 4300 1

28 7.98 639 1 56 7.7 3390 1


Areas, Warangal District (November, 2012).

S. No pH EC F

S. No pH EC F






1 8.14 1369.9 1 24 8.04 1236 1

2 7.84 1751 0.5 25 8.09 2163 0.3

3 7.96 988.8 0.9 26 8.04 2781 0.9

4 7.9 1884.9 0.7 27 7.77 5974 1

5 7.95 1143.3 0.8 28 8.09 1236 2

6 8.02 957.9 0.9 29 8.23 618 2

7 7.47 3193 0.8 30 8.05 3502 2

8 7.76 1957 1 31 7.99 2987 0.8

9 7.82 1864.3 1 32 8.1 2060 2

10 8.62 1071.2 8 33 8.03 2163 1

11 8.27 1060.9 3 34 8.11 1442 0.4

12 8.36 535.6 1 35 8.28 515 0.7

13 7.92 1122.7 2 36 7.84 1545 0.8

14 7.9 1194.8 2 37 7.81 2060 2

15 7.85 1318.4 1 38 8.09 1339 2

16 7.83 4635 3 39 7.88 3193 2

17 7.76 3399 2 40 8 1957 2

18 7.84 4223 3 41 8 1648 1

19 8.1 1751 4 42 8.08 1339 0.6

20 8.32 927 2 43 7.79 2266 2

21 8.24 824 2 44 7.45 1112.4 0.7

22 8.3 515 1 45 7.89 2369 2

23 8.02 1339 1

Table2a: statistical parameters for November 2012

Parameter Min Max Mean Acceptable limit % of samples

exceeding limit

pH 7.45 8.62 8.0 7.0-8.5 2.12

Ec 515 5974 1851 1500 48.9

F 0.3 8 1.65 1-1.5 46.8


Areas, Warangal District (July, 2013).

S. No pH EC F

S. No pH EC F


1 7.9 1512 2.5 29 8.1 1232 2

2 7.88 1904 0.83 30 7.63 3920 0.83

3 8.2 1400 1.4 31 8.09 672 1.4

4 8.19 1232 1.2 32 8.04 896 0.47

5 8.23 2520 2 33 8.17 784 1.4

6 7.84 2464 1.3 34 7.91 2072 0.47

7 8.1 1624 1.7 35 7.85 2128 1.4

8 8.05 1624 1.3 36 7.66 3864 1.7




9 8.22 1232 5.1 37 7.99 840 1.9

10 8.18 1288 2.7 38 8.28 1624 1.9

11 8.19 840 0.85 39 7.93 1792 0.79

12 8.25 672 0.76 40 8.13 2744 1.9

13 8.14 1008 0.86 41 8.15 1848 1.1

14 7.76 2296 1.5 42 8.16 952 0.49

15 8.2 1792 2 43 7.67 1400 0.91

16 8.35 672 1.78 44 7.93 2464 1.3

17 8.1 1568 1.9 45 7.81 3024 1.5

18 7.6 3976 2 46 7.95 1960 0.99

19 7.79 6328 2.5 47 8.04 2016 1.6

20 7.81 9072 3.2 48 8.22 2520 2

21 7.88 2296 4.6 49 7.7 3752 0.49

22 8.1 672 1.9 50 7.48 4200 1.2

23 8.22 560 1 51 7.53 3920 0.55

24 8.2 1064 1.2 52 7.6 3696 0.95

25 8.11 1344 0.52 53 7.77 2800 1.4

26 8.51 392 0.58 54 7.86 1792 0.65

27 7.36 2698 0.53 55 7.87 3080 2.1

28 8.09 1456 1.9 56 8.1 1456 3.2

Table3a: statistical parameters for July 2013

parameter min max mean Acceptable limit % of samples

exceeding limit

pH 7.36 8.51 7.98 7.0-8.5 1.78

Ec 392 9072 2124.17 1500 60.71

F 0.47 5.1 1.53 1-1.5 37.5

4. Conclusions:

Geochemical investigations carried out in the

Narsampet area of Warangal district indicated that.

35.1%, 46.8% and 37.5% of the groundwater samples in

post and pre monsoon seasons exhibit excess fluoride

than prescribed by WHO. Gurjala village and Ayyappa

temple, Maheshwaram recorded unusually high fluoride

concentration i.e 7-8 mg/L. Groundwater in all the three

seasons was neutral to alkaline in nature. High fluoride

groundwater is mainly associated with water which

usually has high pH. Nearly 50% of groundwater of

the study area shows conductivity values higher than

the prescribed limit of 1500 µS/cm for drinking

purpose. In Dwarkapet east central part (17˚92" N

79˚90") during premonsoon electrical conductivity was

extremely high i.e. 9072 µS/cm, which is an indication

of high concentration of dissolved solids


Authors thank the DST-PURSE program for

providing financial assistance in the form of

research project and Head, Department of Applied

Geochemistry, Osmania University, Hyderabad for

providing laboratory facilities.

6. Reference:

[1] Hamilton, M. “Water fluoridation: a risk

assessment perspective”. Journal of Environmental

Health, 54(6), 27–32, 1992.

[2] WHO Fluorides and oral health: report of a WHO

Expert Committee on oral health status and fluoride

use. Technical report 846, Geneva: World Health

Organization, 1994.

[3] Helm, J. D., “The study and interpretation of the

chemical characteristics of natural water”, 3rd edn.

Alexandria, VA: U.S. Geological Survey Water-

Supply Paper 2254, 1985.





[4] Ozsvath, D. L., “Fluoride and environmental

health: a review”. Reviews in Environmental

Science and Biotechnology, 8(1), 59–79, 2009.

[5] Walna, B., Kurzyca, I., & Siepak, J., “ Variations in

the fluoride level in precipitation in a region of

human impact”. Water, Air, and Soil Pollution, 7,

33–40, 2007.

[6] Deshkar, S.M., Deshmukh, A.N. and Vali, S.A.

“Safe limit of fluoride content in drinking water in

different climatic zones of India”. Indian Jour.

Envir. Health, v.2, pp.17-20, 1999.

[7] Susheela A.K, Fluorosis management programme

in India. Curr Sci 77:1250–1256, 1999.

[8] Ramamohana Rao N.V, Rajyalakshmi K, “Endemic

fluorosis in Andhra Pradesh: suggested measures

for prevention and control. In: Proceedings of the

Symposium on Fluorosis”. Indian Academic

Geoscience, 1974.

[9] Sudarshan, V and Rajeswara Reddy, B., “Pollution

of Fluoride in Groundwater and it's impact on

Environment and Socio-economic status of the

people-A case study in Sivannagudem area of

Andhra Pradesh, India”. Indian Journal of

Environmental Protection, Vol.11, No.3, pp.185-

192, 1991.

[10] Govardhan Das. S.V and Sudarshan, V., “Major ion

geochemistry of fluoride rich groundwater,

Markapur area, Prakasam district, Andhra Pradesh,

India”. Environmental Geochemistry, Vol. 6, No.

1&2, pp. 13-20, 2003.

[11] Sunitha, V, Sudarshan, V. and Rajeswara Reddy, B.

“Hydro geochemistry of groundwater, Gooty area,

Anantapur District, Andhra Pradesh, India”.

Pollution Research, Vol. 24 (1), pp. 245-252, 2004.

[12] V. Sudarshan and S. V. Govardhan das, “Nitrate

and Fluoride Distribution in the Groundwater of

Markapur Area, Prakasam District, Andhra

Pradesh, India”, International Journal of Earth

Sciences and Engineering , 05 (01): 101-109, 2012.

[13] Sundaraiah, R., Sudarshan, V., Madhusudhan, N.,

Ashok, K., & Kumar, M. R., “Geochemistry of

groundwater in Kalwakurthy area, Mahabubnagar

district of Andhra Pradesh with special reference to

fluoride distribution”. Journal of Applied

Geochemistry, 15(2), 238-249, 2013.

[14] Narsimha, A., and V. Sudarshan.

"Hydrogeochemistry of groundwater in Basara

area, Adilabad District, Andhra Pradesh, India."

Journal of Applied Geochemistry 15.2: 224-237,

2013.

[15] Narsimha, A., Sudarshan, V., Srinivasulu, P.,

Vishnu, B., Kumar, M. R., & Kumar, S. N..

“Groundwater Quality and its Suitability for

Drinking and Agricultural Purpose Around Chityal

Area, Nalgonda District, Andhra Pradesh, India”.

Water Res. Dev, 2(3), 68-75, 2012.

[16] Shortt HE, McRobert G.R, Barnard T.W,

Mannadinayer A.S., “Endemic fluorosis in Madras

Presidency”. Indian J Med Res 25:553–561, 1937.

[17] Pandit C. G., Raghava Chary, Rao T. N. S. and

Krishna Moorthy V., “Endemic fluorosis in South

India”. Indian J. Med. Res., Vol. 28. Page 533,

1940.

[18] Tiwari, A. K., Dikshit, R. P., Tripathi, I. P., &

Chaturvedi, S.K., “Fluoride content in drinking

water and ground water quality in rural areas of

Tehsil Mau district, Chitrakoot”. Indian Journal of

Environmental Protection, 23(9), 1045–1050, 2003.

[19] Nawlakhe, W. G., & Bulusu, K. R., “Water

treatment technologies for removal of excessive

fluoride”. In C. P. Gupta (Ed.), Appropriate

methodologies for development and management of

ground water resources in developing

countries,Vol. 2, pp. 815–828, 1989.

[20] Gupta and Banerjee, “Fluoride accumulation in

crops and vegetables and dietary intake in a

fluoride-endemic area of west Bengal”, research

report fluoride, 44(3)153–157, 2011.

[21] APHA, Standard Methods for Examination of

Water and wastewater. 15th

Ed. American Public

Health Association, Washington D. C., 1985.

[22] WHO. Guidelines for drinking water quality.

World Health Organization, 3rd

Edition. Geneva,

2004.

www.cafetinnova.org

Indexed in





ISSN 0974-5904, Volume 07, No. 01

February 2014, P.P.213-222


Ore Microscopic Study of the Gold Mineralization within Chandil

Formation, North Singhbhum Mobile Belt, Eastern India

KARUN KUMAR CHANDAN, VANDANA JHA, SUBRATA ROY, MOUSOMA KHATUN, PRABODHA R.

SAHOO AND SAHENDRA SINGH Indian School of Mines, Dhanbad, 826004, Jharkhand, India

Email: [email protected]

Abstract: The Palaeo- to Meso-Proterozoic North Singhbhum Mobile Belt (NSMB) refers to the assembly of

multiphase folded, low to medium grade meta-sedimentary and meta-igneous rocks of Proterozoic age (1.0–2.4 Ga),

lying between the Archean Singhbhum Craton in the south, and the Meso/Neo-Proterozoic (0.9–1.7 Ga)

Chotanagpur Gneissic Complex (CGC) in the north. Gold occurrences of moderate concentration have been reported

from different parts of NSMB within the volcano-sedimentary and meta-sedimentary rocks like quartzite, schist,

phyllites etc. The auriferous mineralization is associated with sheared rocks that are traversed by veins of quartz and

quartz-calcite. Gold occurs mainly in association with sulfides like pyrite, arsenopyrite, pyrrhotite, chalcopyrite,

sphalerite, etc. Arsenopyrite and pyrite are closely linked with gold occurrences in the area. The gold seems to occur

as occluded grains within quartzite and is quite pronounced when arsenopyrite is of finer in size. The mineralization

is structurally controlled and is associated with latter stages of deformation.

Keywords: NSMB; Gold mineralization, Singhbhum Craton, Archean, Proterozoic, Chandil Formation.

1. Introduction:

The Singhbhum crustal province extends from south

eastern part of Jharkhand to north of Orissa and exposes

a vast tract of Precambrian rocks occupying an area of

approximately 40,000 km2

[1]. North Singhbhum

Mobile Belt (NSMB) [2], also referred to as the North

Singhbhum Fold Belt (NSFB), is situated south of the

Chotanagpur granite gneiss (CGC) [3-5]. NSMB

consists of comparatively younger, Singhbhum Group

of rocks [6]. The southern part consists of older Iron

Ore Group (IOG) rocks [6] and is also known as

Archean granite-greenstone terrain or Singhbhum

Granite Craton [3, 7, and 8].

Radiometric data from rocks of this crustal province

indicate an age ranging from 3500 Ma to 1400 Ma [2, 7,

9]. Earlier workers have identified three distinct petro-

tectonic zones within the Singhbhum crustal province

[10, 11]. From south to north, these are: (1) the southern

Archean granite – greenstone terrain [8, 7, 12], widely

referred to as the Singhbhum Granite Craton; (2) the

almost 200 km long North Singhbhum Fold Belt

(NSFB) comprising the Dhanjori, Chaibasa, Dhalbhum,

Dalma and Chandil Formations [4, 13-15], and (3) the

extensive granite-gneiss and migmatite terrain in the

north, known as the Chotanagpur Gneissic complex

(CGC). A zone of sheared and deformed rocks (the

Singhbhum Shear Zone, SSZ); [16-20] was developed

close to the contact of the oldest Proterozoic

supracrustal (Dhanjori) belt with the Archean nucleus.

Small linear granitic bodies are present along the SSZ

[19]. The Rb–Sr whole rock age of about 1600 Ma

obtained from these granites has been inferred to reflect

the age of metamorphism of the sediments due to

thrusting along the SSZ [20-22]. An account of gold

mineralization in the Chandil Formation of NSMB,

particularly of ore microscopic study, is presented in

this paper.

2. Generalized Stratigraphy of the Singhbhum

Craton:

Three principal components that make up the Archean

nucleus of Singhbhum are the Older Metamorphic

Group (OMG), massifs of Singhbhum Granite and

younger supracrustal rocks. Generalized chrono-

stratigraphic succession according to their ages and

position is given in Table1. Older metamorphic Group

(OMG) rocks are the oldest rocks that occur south of

Singhbhum Shear zone [23]. The rocks consist

predominantly of amphibolitic facies, politic schists,

quartz-magnetite-cummingtonite schists, quartzite,

banded calc-gneiss and para- and ortho-amphibolites.

2.1. Structure of Singhbhum-Orissa Craton:

The rocks of Singhbhum fold belt show three phases of

deformations, as evident by the linear and planar

structural features [24]. The first generation of planar

structures is the metamorphic imprint formed by F1

214 Ore Microscopic Study of the Gold Mineralization within Chandil Formation, North




folding within the recrystallized rocks of the Singhbhum

Group. In Chaibasa Formation of the Singhbhum

Group, the F1 folds are few and small, and are

characterized by reclined geometry, found at places as

rootless hinges with mineral lineation (L1) due to

intersection of S0/S1. The second phase of folding (F2) is

generally coaxial with F1 (Figures 3a, b) and gave rise to

E-W regional folds with a strong axial plane foliation

(S2) that is recognized as regional foliation in the

terrain. Secondary foliation is developed at low angle to

the bedding, defining the blunt hinged synformal

closures and puckered nature of the S0. The large scale

folds in the bedding schistosity are considered the

outcome of F2 in the Galudih near Ghatsila [24]. The F2

fold in the northern belt is asymmetric and indicates that

rocks in the north have moved upwards relative to the

rocks of the south. In the southern part, the F2 fold is

upright in nature with regional foliation maintaining a

vertical attitude.

Fig1: Geological map of Jharkhand [25]

Table1: General Stratigraphy of Singhbhum Craton [26]

Age

(Ga) Singhbhum Nucleus

Singhbhum - Dhalbhum

Mobile Belt Chotanagpur Belts

0.9-1.6 Newer dolerite Syn-to-late- and post-tectonic,

granites/gneisses

1.5 Kolhan Group

1.6 Mayurbhanj Granite;

Gabbro/anorthosite

Chakradharpur Granite;

gabbro/anorthosite Gabbro/anorthosite

Ultramafic intrusion

----------------------------------------------Unconformity-------------------------------------------------

Dhanjori Group Dalma lavas

----------------------------------------------Unconformity-------------------------------------------------

Singhbhum Group Singhbhum Group

215 KARUN KUMAR CHANDAN, VANDANA JHA, SUBRATA ROY, MOUSOMA KHATUN,

PRABODHA R. SAHOO AND SAHENDRA SINGH



(Chaibasa formation)

----------------------------------------------Unconformity-------------------------------------------------

2.9 Singhbhum Granite;

Iron Ore Group Orthogneisses

----------------------------------------------Unconformity-------------------------------------------------

3.8 Older Metamorphic

Group Gneisses

Older Metamorphic

Group supracrustal rocks

Basement? Basement? Basement?

Fig2: Location Map [27]

2.1.1. Gold Mineralization in North Singhbhum

Mobile Belt:

The North Singhbhum Mobile Belt (NSMB) is a 200

km long E-W trending linear fold belt, sandwiched

between the SGC in the south and CGC in the north.

The major rock types of this area are chloritic schists of

basic volcanic and heterogenous assemblage, indistinct

soda-granite/ feldspathic schists, Arkasani granite-

granophyre, quartzite, tourmalinite, conglomerates,

sericitic and biotitic schists and mylonites. Structural

evidences from the area show signs of shearing, as

evident by deformation pattern, more than one phases of

folding, mylonitization and rotation of linear fabric and

stretching along the direction of movement. Several sets

of folds are developed in this area [28]. Despite all the

above mentioned litho-structural criteria, the bulk of

NSMB lithology is made up of the derivatives of basic

volcano-clastic and exhalative material during the

waning stage of the volcanism along cratonic margin.

The area consists of economic deposits of copper,

uranium, phosphate, silver, gold and tellurium. The gold

particles appear to be of higher fineness. An average of

500-600 kg per annum of gold is also being recovered

as a byproduct from the copper mineralisation from this

belt. Gold is also found in intimate association with

pyrite, as invisible gold in the meta-sediments of the

region [29].





2.1.2. Structural control on gold mineralization in the

area:

Au mineralization is structurally controlled and occurs

within the tuffaceous quartzose phyllite with

intercalated quartzite [30]. This lithounit is

characterized by strong foliation (S1) and stretching

lineation. Stretching of quartz grains led to the

formation of quartz ribbons. In quartzose part, very fine

polygonal quarts grains are present and quartz ribbons

are recrystallised.

Petrographic studies show fine- to medium-sized

aggregate of arsenopyrite grains that occur along the

schistosity. The arsenopyrite bands follow the quartz

veins and fill the fractures of the schistose rock. At

places, arsenopyrite occurs along with pyrite and both

have experienced late fracturing together with the

introduction of quartz-carbonate veins that contain

native gold. Gold is occurring as free phase, mainly in

association with arsenopyrite. Quartz - carbonate veins

occur along shear planes in chlorite-quartz schist.

2.1.3. Petrographic Characteristics of Host rocks:

Detailed petrographic studies of the rock types were

carried out for samples collected mostly from surface

exposures along the nala (small stream) sections and

also from the core samples. Thin and polished sections

were studied to identify the mineral assemblage and also

to understand the control on gold mineralization.

Approximately 100 representative samples were

collected from the area. Representative thin sections

have been studied in detail to see the textural and

mineralogical variations along with the effects of

alteration, including hydrothermal alterations.

Fig3: Field photograph showing a) & b) hook shaped fold in cherty phyllite, c) Presence of three sets of foliation in

phyllite in Sindauri area, d) Two sets of foliation in Sindauri area, e) Two sets of perpendicular joint set in phyllite

in Sindauri area and f) Formation of quartz boudins in ferruginous quartzite, exposed along the shear zone in

Parasi area.

2.1.4. Ore Mineralogy and Textural Features:

In the study area, the sulfide and oxide ore minerals are

concentrated within the quartz veins that are traversing

through the host rocks, i.e., quartz-magnetite-biotite-

sericite schist and amphibolites. Around 15 thin and

polished sections were studied for the detailed ore

microscopic observations.

The principal ore minerals and their associated gangue

minerals are as follows:

Native Metal: Gold.

Sulfides: Pyrite, pyrrhotite, arsenopyrite, chalcopyrite,

sphalerite,.

Oxides: Magnetite, ilmenite

Gangue Minerals: Quartz, biotite, sericite, tremolite,

actinolite, hornblende and epidote.

Details of different types of ore minerals found in the

area are presented below.

Gold: Gold mineralization is structurally controlled by

phyllite ± tuff with intercalated quartzite. The auriferous

mineralization is mostly found in sulfide minerals





(Figure 5). Arsenopyrite, pyrite and rarely pyrrhotite are

in the descending order of abundance.

Gold mineralization within the area has been observed

preferably in association with sulfide mineralization,

i.e., pyrite and arsenopyrite. The auriferous

mineralization is in the form of disseminated specks,

stringers, fracture-fillings, streaks and veinlets. Effect of

shearing is well evident by the presence of stretched

mineral grains and the presence of mylonite and

ultramylonite. Morphologically, the size of native gold

is variable and ranges from microns or as inclusions

within sulfides to large visible form, called as nuggets.

Pyrite: Pyrite is the most abundant sulfide phase with a

size variation from 0.01 mm to 5 mm and occurs mostly

as euhedral to subhedral cubes and pyritohedrons of

different generations. Pyrite is stable over a wide range

of sulphur activity and because of its high thermal

stability (7420C at low pressure), it is stable up to the

highest grades of metamorphism. Pyrite grains within

the quartz veins are highly fractured and sheared.

Within the chlorite schist and carbon phyllite, grains are

perfectly euhedral and are not affected by any

deformation. In some sections, typical euhedral and

cubic grains of pyrite is also observed, which are

possibly of later generation (Figure 4). Pyrite has also

been found to occur within the quartz veinlets of

metabasics. Micro-scale fracturing in pyrite and

arsenopyrite suggests the effect of deformation. The

gold-bearing pyrite grains are pervasively fractured and

are of having irregular grain boundaries. Gold

inclusions of 2μm to 50μm size are seen within the

fractures of pyrite grains.

Pyrrhotite: Pyrrhotite is associated with arsenopyrite,

pyrite, chalcopyrite, sphalerite and also with galena. In

order of abundance, pyrrhotite is lesser than pyrite and

arsenopyrite. It occurs generally as irregular,

allotriomorphic deformed grains associated with

chalcopyrite (Figures 4, 6, 7). They occur in quartz

veins and also in the altered wall rock zones, either in

the form of individual grains or inclusions within the

arsenopyrite, pyrite and chalcopyrite. In mineralized

quartz veins, pyrrhotite is associated with sulfides like

arsenopyrite, pyrite, chalcopyrite and sphalerite,

whereas in magnetite-quartz-biotite-sericite schist and

carbonaceous phyllite, they have been observed in

association with pyrrhotite. Pyrrhotite, occurring within

the quartz veins, is altered to pyrite at the peripheral

zones. These pyrites are texturally different from the

first generation pyrite and lack the typical euhedral

shape (Figure 5). It also shows replacement texture

along with galena, pyrite, chalcopyrite (Figure 4, 6, 7).

Arsenopyrite: This is used as a pathfinder mineral for

gold exploration in the study area. It often contains

inclusions of chalcopyrite, sphalerite and pyrrhotite.

Three different generations of arsenopyrite have been

observed. They occur as idiomorphic crystals with

characteristic rhombohedra shape and, at places, show

sign of mylonitic deformation. This arsenopyrite

exhibits deformational fabric and cataclastic texture

along the shear fracture within the shear zone.

Arsenopyrite grains are also fractured and are replaced

by fractured pyrite.

Chalcopyrite: Chalcopyrite is present in lesser amount,

as compared to other sulfides like pyrite, pyrrhotite and

arsenopyrite. It occurs as disseminations, stringers and

along fracture-filled thin veinlets, in association with

pyrrhotite, pyrite and sphalerite.

Galena: Galena is present in comparatively very lesser

amount and occurs in association with pyrrhotite, pyrite,

chalcopyrite and sphalerite. These grains commonly

occur in irregular and anhedral to subhedral form, and

are medium grained, with their size being of ~ 0.6 mm

(Figure 4).

Magnetite: Magnetite occurs in association with pyrite

and pyrrhotite. Its grains are commonly idiobalstic to

subidioblastic (Figure 4c).

Ilmenite: Ilmenite occurs in association with pyrite and

chalcopyrite. Its grains are generally lath-shaped and

fibrous, and occur as disseminations.

2.1.5. Wall Rock Alteration:

Wall rock alteration is a common feature, associated

with hydrothermal gold deposits present around the

world [31]. Rocks of Chandil formation show

characteristic mineralogical changes in proximity with

the mineralized zone. Petrographic studies revealed that

four major types of alterations have occurred in the area,

viz., sulfidization, chloritization, sericitization and

carbonatization, along with silicification and

biotitization.

Sulfidization:

The common sulfide minerals observed in the area are

pyrite, chalcopyrite, pyrrhotite, arsenopyrite and

sphalerite. Some of the pyrite and arsenopyrite grains

are shattered and crushed, suggesting deformation of

rocks due to shearing activity. Such zones have

undergone strong chemical and mineralogical changes

due to mobility of a number of mobile cations like Fe,

Mg, Ca, and Na. Intense alteration to sulfide is due to

the addition of sulfur +water in the system with low

CO2. Ferromagnesian silicates get enriched with sulfur

and have resulted in the deposition of sulfides.





Fig4: Photomicrographs of thin and polished sections (under XPL) of rocks sample showing a) pyrite, chalcopyrite

and ilmenite grains, and replacement texture between chalcopyrite and pyrite in schistose quartzite; b) pyrite,

chalcopyrite and galena grains, and replacement texture among them in schistose quartzite; c) pyrite, chalcopyrite

and magnetite grains, and replacement texture among chalcopyrite, pyrite and magnetite in schistose quartzite; d)

galena and pyrrhotite grains, and replacement texture between galena and pyrrhotite in schistose quartzite; e)

replacement texture between chalcopyrite and pyrrhotite; f) 2nd

generation pyrrhotite mineralization along the fault

plane in quartzite.

Fig5: Photomicrographs of thin polished sections (under XPL) of rocks sample showing a) occurrence of pyrrhotite

along the limbs of quartz vein in schistose quartzite; b) occurrence of chalcopyrite along a fold in schistose

quartzite; c) micro faulting in quartz vein in schistose quartzite; d) & e) ductile deformation in quartz vein in

schistose quartzite; f) micro-faulting in quartz vein.





Fig6: Photomicrographs of thin polished sections (under XPL) of rocks sample showing a) & b) occurrence of

pyrrhotite along a quartz vein in schistose quartzite; c) & d) En echelon texture in pyrrhotite in schistose quartzite;

e) pyrrhotite showing twining, separating coarse grained quartz from fine grained quartz; and f) alignment of

pyrrhotite grains along F2 plane.

Fig7: Photomicrographs of thin polished sections (under XPL) of core sample showing a) deformed pyrrhotite in

schistose quartzite; b) disseminated pyrrhotite in schistose quartzite; c) en echelon texture in pyrrhotite in schistose

quartzite; d) replacement texture between chalcopyrite and pyrrhotite in schistose quartzite; e) occurrence of

pyrrhotite along with pyrite and little amount of chalcopyrite; and f) pyrrhotite, replaced by chalcopyrite.





Fig8: Gold grains along the grain boundaries of quartz (observed under XPL)

Chloritization:

This is a prevalent alteration process, observed in

almost all the types of gold deposits and is characterized

by the dominance of chlorite. Chloritization is the most

common type of alteration in the area. In field, greenish

appearance makes it readily distinguishable from other

alteration zones. Mineral assemblage in this zone

contains

Chlorite + hornblende + actinolite + calcite + sericite

+ quartz

Chlorite is a secondary mineral formed by hydrothermal

alteration of hornblende and actinolite of the host rock.

The common reaction is expressed as: (Fe, Mg) silicate

+ H20 + CO2 + K → Chlorite + Calcite + Quartz +

Sericite.

Sericitization:

This is a common alteration process in epigenetic gold

deposits and named after the prevalent white mica, i.e.,

sericite. The general mineral assemblage in this zone is

sericite + muscovite + quartz + chlorite + calcite +

albite. Sericite may have been formed due to hydration

of feldspars or by alteration of other silicates (i.e.,

rearrangement of K, Al and SiO2). This process involves

the introduction of K and H2O into the rocks and

removal of mobile cations like Fe, Ca and Mg. The

appearance of white mica can be as per the following

reactions:

Mg-chlorite + Ankerite/Cal + CO2 + K → Ankerite/

Cal + Sericite; Qtz + H2O/Chloite + K → Sericite +

Quartz + H2O

Carbonatization:

This process is marked by the formation of secondary

carbonates in the host rock. The secondary carbonates

incorporate calcite, ankerite and dolomite. Calcite and

dolomite have been detected away from the quartz

veins, while ankerite is found closer to the quartz veins.

Proportion of quartz and muscovite is higher compared

to the zone of chloritization. The interaction between

fluid and mafic rocks has resulted in the formation of

quartz-carbonate association, which can be expressed as

a general equation: (Fe, Mg,) silicate + CO2 → Calcite /

ankerite + Quartz + H2O. It is apparent from the

reaction, that the fluid is relatively carbonaceous during

the growth of the zone. Carbonatization is of rare

occurrence in the area.

Mineral Stage-I Stage-II Stage-III

Quartz

Sericite

Ilmenite

Magnetite

Carbonate

Pyrrhotite

Pyrite

Arsenopyrite

Sphalerite

Chalcopyrite

Galena

Gold

Fig9: Paragenetic sequence of gold and associated sulfide & oxide ore minerals, and gangue minerals, established

based on their textural relationships in the study rocks.





2.1.6. Paragenesis:

Stage I: The earliest minerals to be formed are quartz,

sericite, ilemenite and pyrrhotite. The sulfides,

occurring in disseminated form during this stage, are

arsenopyrite I, pyrite I, pyrrhotite I, chalcopyrite I

within the mafic rocks surrounding the auriferous quartz

veins. Pyrite I and arsenopyrite I are generally

idiomorphic. Although gold grains are not observed at

this stage during the microscopic studies, possibly the

invisible gold content within the primary sulfides got

entrapped, which was later released due to

remobilization. The primary sulfides carrying the

invisible gold are arsenopyrite, pyrite and chalcopyrite.

Stage II: This stage is characterized by the sulfide

mineral phases, especially arsenopyrite II, which is

associated with sphalerite along with pyrite II and

pyrrhotite II, mainly restricted to shear planes.

Magnetite also occurs in this stage. The arsenopyrite II

is associated with sphalerite along with pyrite II and

pyrrhotite II to a lesser extent. Small gold inclusions

within the sulfide phases and the silicate phases,

detected during the microscopic studies, were formed

during this stage.

Stage III: Post-depositional redistribution of the gold

within the lattice structure of arsenopyrite, pyrite,

galena and sphalerite resulted in the deposition of native

gold. Native gold is present in the fractures and along

the grain boundaries of the sulfides, indicating late

phase of gold deposition. Arsenopyrite III, pyrrhotite III

and Chalcopyrite III have also been observed. In this

stage, medium concentration of gold has been observed.

This may be due to the late remobilization leading to the

formation of rich ore shoots.

3. Conclusions:

The Palaeo- to Meso-proterozoic Chandil formation

constitutes the northern part of North Singhbhum

Mobile Belt between Dalma volcano-sedimentary belt

in the south and Chhotanagpur granite gneiss at its

north. The major rock types observed in the area include

magnetite-biotite-quartz-sericite schist, ferruginous

quartzite, phyllite with intercalated schistose quartzite,

carbon phyllite with intercalated grey (carbon) quartzite,

acid-tuff, ultramafic (tremolite-actinolite bearing)/

metabasic intrusives, vein quartz and quartz-calcite

veins. General trend of the most dominant foliation

varies from NE-SW to WNW-ESE, having steep dips

on either side. S2

schistosity is the most pervasive

foliation in the area. Micro- and meso-folds have a low

westerly plunge. The tuffaceous phyllite (mylonitized)

represents a ductile shear zone. Petromineragraphic

studies indicate that fine to medium size aggregate of

arsenopyrite grains occur along the schistosity. The gold

mineralization in the area is found to be associated with

this shear zone, especially in the quartzite part.

4. Acknowledgments:

The authors are very much thankful to the Director

Indian School of Mines, Dhanbad for his permission to

publish this paper. Thanks are also to all the reviewers

for their excellent suggestions and effort, which

immensely improved the quality of our manuscript.

5. Reference:

[1] S. Misra and P.T Johnson (2005) -

“Geochronological constraints on evolution of

Singhbhum Mobile Belt and associated basic

volcanics of Eastern Indian Shield”- Gondwana

Research, Vol. 8(2), p.129-142.

[2] K.S. Mishra (1998) - "Occurences of polymetallic

(Fe-U-Au-REE) iron-oxide breccia type

mineralization around Chandil, West Singhbhum

District, Bihar"- Abstract Proceedings, National

Seminar on Geoscientific Advances in Bihar, India

in the last decade. Geological Survey of India,

Operation Bihar, Patna, p. 56.

[3] S.K. Acharyya, A. Gupta and Y. Orihashi (2008) -

"U-Pb zircon dates (LA-ICP-MS) of some felsic

magmatic rocks from the basal part of Dhanjori

basin and their stratigraphic implication, Eastern

Singhbhum, India"- IAGR Conference, Abstract,

Series 5 on ‘Tectonics of Indian Subcontinent’

(TOIS), IIT-Mumbai, p. 151-152.

[4] A. Gupta and A. Basu (2000) - "North Singhbhum

Proterozoic mobile belt, Eastern India—a review"-

Spec Publ - Geol Surv India, Vol. 55, p. 195–226.

[5] H.N. Bhattacharya and S. Mahapatra (2007) -

"Evolution of the Proterozoic rift margin

sediments—North Singhbhum Mobile Belt,

Jharkhand-Orissa, India" - Precambrian Research.

Vol. 162, p. 302–316.

[6] S.N. Sarkar and A.K. Saha (1962) - "A revision of

Precambrian stratigraphy and tectonics of

Singhbhum and adjoining regions"- Quarterly

Journal of Geological Mining Metallurgical

Society of India, Vol. 34, p. 97-136.

[7] S. Sengupta, G. Sarkar, A.K. Ghosh Roy, S.K.

Bhaduri, S.N. Gupta, and A. Mandal (2000) -

"Geochemistry and Rb-Sr geochronology of acid

tuffs from the northern fringe of the Singhbhum

craton and their significance in the Precambrian

evolution"- Indian Minerals, Vol. 54(1-2), p. 43-

56.

[8] D. Mukhopadhaya (2001) - "The Archaean nucleus

of Singhbhum: the present state of knowledge" –

Intl. J. Gondwana Res., Vol. 4, p. 307– 318.

[9] N. Sengupta, D. Mukhopadhyay, P. Sengupta and

R. Hoffbauer (2005) - "Tourmaline-bearing rocks





in the Singhbhum shear zone, Eastern India:

Evidence of boron infiltration during regional

metamorphism" - American Mineralogist, Vol. 90,

p. 1241-1255.

[10] M.K. Bose (1994) - "Sedimentation pattern and

tectonic evolution of the Proterozoic Singhbhum

Basin in the eastern Indian shield"-

Tectonophysics, Vol. 231, p. 325–346.

[11] S.C. Sarkar, A. Gupta and A. Basu (1992) – “North

Singhbhum Proterozoic mobile belt, Eastern India:

its character evaluation and metallogeny, in Sarkar

S.C., ed., Seminar volume, Metallogeny related to

Tectonic of Proterozoic Mobile Belts" - Oxford-

IBH Publisher, New Delhi, India, p. 271-305.

[12] S.K. Acharyya (1993) - "Greenstones from

Singhbhum craton, their Archaean character,

oceanic crustal affinity and tectonics" – Proc. Natl.

Acad. Sci. India, Sect. A; Vol. 63, p. 211 –222.

[13] S.K. Acharyya (2003) - "The nature of

Mesoproterozoic Central Indian Tectonic Zone

with exhumed and reworked older granulites" –

Intl. J. Gondwana Res., Vol. 6, p. 197– 214.

[14] S.K. Acharyya (2003) - "A plate tectonic model for

Proterozoic Crustal evolution of Central Indian

Tectonic Zone" - Gondwana Geol. Mag., Vol. 7, p.

9– 31.

[15] A. Gupta and A. Basu (1991) - "Evolutionary trend

of the mafic–ultramafic volcanism in the

Proterozoic North Singhbhum mobile belt" -

Indian Miner., Vol. 45, p. 273–283.

[16] S.K. Ghosh and S. Sengupta (1987) - "Progressive

development of structures in a ductile shear zone"-

J. Struct. Geol., Vol. 9, p. 277–287.

[17] S. Joy and D. Saha (2000) - "Dynamically

recrystallised quartz c-axis fabrics in greenschist

facies quartzites, Singhbhum shear zone and its

footwall, eastern India—influence of high fluid

activity"- J. Struct. Geol., Vol. 22/6, p. 777–793.

[18] D. Mukhopadhaya (1984) - "The Singhbhum Shear

Zone and its place in the Evolution of the

Precambrian mobile belt of North Singhbhum.

Proc seminar on Crustal evolution of the Indian

Shield and its bearing on metallogeny" - Indian J.

Earth Sci, Seminar Volume, p. 205–212.

[19] A.K. Saha (1994) - "Crustal evolution of

Singhbhum-North Orissa, Eastern India" - Memoir

Geologial Society of India, Vol. 27, p. 341.

[20] R. Mazumder and S. Sarkar (2004) -

"Sedimentation history of the Palaeoproterozoic

Dhanjori Formation, Singhbhum, eastern India" -

Precambrian Res., Vol. 130, p. 269–89.

[21] S.C. Sarkar (1994) - "The metallogeny and

production of gold: The world scenario and the

Indian situation"- Indian Journal of Geology, Vol.

66(4), p. 237-278.

[22] Gupta and A. Basu (2000) - "North Singhbhum

Proterzoic Mobile Belt, Eastern India-A review" -

Geological Survey of India, Special Publication

No. 55, p. 195-226.

[23] K.C. Condie (1983) - "Plate Tectonics and Crustal

Evolution"- 2nd

ed. Pergamon Press, New York, p.

217-227.

[24] M. Deb and J. Richard Goldfarb (2010)- "Gold

Metallogeny in India and beyond"- Narosa

Publishing House, India, p. 256-276.

[25] T.M. Mahadevan (2002) – “Geology of Bihar and

Jharkhand”, First edition, Geological Society of

India, Bangalore.

[26] M. Ramakrishnan and R. Vaidyanathan (2008) -

“Geology of India”, Vol. 1, Geological Society of

India, Bangalore.

[27] S. Mahato, S. Goon, A. Bhattacharya, B. Mishra,

and H.J. Bernhardt (2008) – “Thermo-tectonic

evolution of the North Singhbhum Mobile Belt

(eastern India): a view from the western part of the

belt” - Precambrian Research, Vol. 162, p. 102-

127.

[28] S. Singh, K.K. Chandan, V. Jha and A.S.

Venkatesh (2013) - "Metallotectonic Evolution and

its Implications on the Exploration Prospects of

Orogenic Gold Mineralization within North

Singhbhum Mobile Belt, Singhbhum Craton,

Eastern India." - Proceedings volume Second

Symposium on the Geological Resources in the

Tethys Realm, 5-8 January, 2013, Aswan, Egypt

[29] V. Jha, K.K. Chandan, M. Khatun, S. Singh and A.

S. Venkatesh (2013) - "Geology and Geochemistry

of Gold bearing Meso-Proterozoic Chandil

Formation, North Singhbhum Mobile Belt, Eastern

Indian Craton: Evidences form Trace - REE

signatures, EPMA and SEM analysis." - Abstract

accepted in ESEMR-2013, held at ISM, Dhanbad,

p. 53.

[30] K.K. Chandan, V. Jha, K. Sairaj, S. Singh, A.S.

Venkatesh (2013) - "Greenfield Exploration

Prospects of Orogenic Gold Mineralization in and

around Lawa Area, North Singhbhum Mobile belt,

Eastern Indian Craton" - International Journal of

Applied and Natural Sciences(IJANS), Vol. 2,

Issue 4, p. 2319-4022.

[31] R.W. Boyle, (1979) - “Geochemistry of gold

deposits” - Geol. Survey, Canada Bull., p. 280.

www.cafetinnova.org

Indexed in





ISSN 0974-5904, Volume 07, No. 01

February 2014, P.P.223-229


Applications of Expert Systems in Mining Industry: A Review

K. RAM CHANDAR AND H. AGARWAL Department of Mining Engineering, National Institute of Technology, Karnataka, Surathkal, Srinivasnagar-575025,

Mangalore, D.K, INDIA


Abstract: An Expert System is a computer system that emulates the decision-making ability of a human expert.

Expert systems are designed to solve complex problems by reasoning about knowledge, like an expert, and not by

following the procedure of a developer as is the case in conventional programming. The first expert systems were

created in the 1970s and then proliferated in the 1980s. In this paper, an effort has been made to sum up some of the

useful expert systems and their working principles for effective use in mining operations.

Keywords: expert system, optimization, logic blocks.

1. Introduction:

In present days, in a mine, many of the processes

include the involvement of more sophisticated machines

along with the skilled labour need to tackle the

problems which arise during mining operations.

Sometimes, it becomes necessary to use artificial

intelligence and expert systems due to the time available

for a particular action during mining operation or due to

generation of new mining problems. In order to make

mining activity more reliable and highly mechanized

along with the application of expert systems, research

should be focused on in applying ‘intelligent expert and

support systems’ in different activities related to mining.

In the last two decades, a boon has been observed in

different sectors of every industry like time

management, cost management, productivity sector,

etc., In order to meet these requirements, the investment

was done on reliable machinery. These demands these

intelligent expert system as well as support systems

which uses artificial intelligence (AI) to carry out a

logical solution for a common as well as new and

complicated problems faced by mining and other

industries.

In mining industry, several problems like estimation of

ore reserves in complex geometrical conditions,

optimum mine planning, suitable selection of

equipment, slope stability, ground control, blast design,

man power distribution, budget planning, mineral

processing etc., to be carried out effectively to suit the

given geo-mining conditions, maintenance of

equipments and many more complex problems are there

which require implementation of AI based systems or

expert systems. Ram Chandar et al. (2001) has

developed an expert program to predict rock

fragmentation. Ram Chandar (2002) has developed a

program for computer aided hydraulic stowing system.

Ram Chandar & Singh (2002) developed a program for

design of support system in underground coal mines.

Sastry & Ram Chandar (2008) has done simulation

studies in assessing the role of initiation system and

pattern on blast performance. Sastry & Ram Chnadar

(2010) and Trivedi, et al (2012) have used numerical

modelling in design and stability analysis of slopes.

Vishal et al. (2011) and Sarkar, et al. (2012) have used

various statistical tools in estimating the rock properties.

A typical basic expert system is shown in Figure. 1.

Fig1: Typical expert system structure (C. Kirmanli and

S.G. Ercelebi, 2009)

2. Basic principles of expert systems:

The basic principles on which expert systems mainly

depend are:

1. Lifetime distribution models.

2. Markov model.

3. Fault tree analysis.

224 Applications of Expert Systems in Mining Industry: A Review



Though all these principles give a good estimation and

prediction of desired data but when it comes to accurate

results, one has to consider new methods and

technology.

Systems accuracy and reliability generally try to change

with time. Thus one can say that these changes are time

series process. Predicting the variability of reliability

with time is a too difficult task. The main difficulty

arises when one has to assume failure distributions and

due to lack of appropriate technology to forecast these

assumptions, it becomes more difficult. The auto-

regressive moving average (ARMA) model has been

one of the most popular approaches in time series

prediction (Box and Jenkins, 1976).

Recently, Artificial Neural Networks (ANNs) have

received growing attention in time series forecasting

(Chatterjee and Bandopadhyay, 2012). The Support

Vector Regression (SVR), is a widely used and

preferred data-driven technique for time series

forecasting.

Fault tree analysis (FTA) is a top down, deductive

failure analysis in which an undesired state of a system

is analysed using Boolean logic to combine a series of

lower-level events. This analysis method is mainly used

in the field of safety engineering and reliability

engineering to determine the probability of a safety

aspect or a particular system level (functional) failure.

In mining industry, FTA and FTA based expert systems

can be used in:

1. Understanding logic of an operation which leads

to undesired state/condition during the use of

machine.

2. Increasing system safety by monitoring and

controlling system processes in an efficient way.

3. Minimizing and optimizing resources.

4. Creating the Critical Equipment/Parts/Events lists

for different important measures.

Some of the expert systems developed for mining

applications are described below.

3. Development of expert systems of mine

ventilation systems:

There is no need of complex expert shell in designing

expert systems (ES) for mine ventilation. During ES

development it is necessary to take account the

behaviour of main system of safety which is ensuring

main ventilation system. Expert systems and programs

should be designed in such a way that it can calculate

and distribute the required amount of air in mine

workings during normal as well as abnormal conditions

(example -fire in underground mine).

At the time of creating ES on safety for mining one has

to consider certain properties of the required

information, and should mention the application area of

that ES. The data which is used as an input for ES

should include information about mine workings that

include the following fields: number in order, initial and

end nodes, its cross section, marks of height of its start

and finish, aerodynamic resistance, initial and final

temperature on mine workings and so on.

It may happen that at the time of entering the data by

mistake wrong data get feds into the ES, so in order to

decrease mistakes and for getting most reliable results

one can use ICXINFOR and WENTCHAR (Koketayev,

2003). These programs can also be used for verification

of reliability of initial data programs.

When a person has to deal with the program which

calculates and determines the amount of air to be

distributed in mine ventilation system, WENTCHAR

use is preferred, for using this program it is necessary to

give characteristics of fans.

In order to automatically carry out the calculation of

branches and nodes of ventilation system and testing

presence of missed branches ICXINFOR program is

used. Input file of this program includes number of a

branch, initial node, and end node.

Expert systems should be designed in a way that any

enquiry can be made at any point during the working

operation.

According to Koketayevs’ (2003), an expert system for

mine ventilation should contain the following programs:

1. Programs like ICXINFOR and WENTCHAR.

2. Program to test correct input of initial data.

3. Program for calculating the coefficient of fan

curves.

4. Programs for view of calculated data.

3.1. ES-VENT: an online expert system:

The ES-VENT (Expert System for Ventilation) is a

knowledge based system which has been developed for

on-line diagnosing of ventilation problems using the

expert system development tool, IITMRULE. The

IITMRULE was developed at Indian Institute of

Technology, Madras, India. The ES-VENT system is

made up of three modules (Bandyopadhyay and Sinha,

2002), namely On-line Data, Diagnostics and

Knowledge Base System (KBS).

Each module is a collection of several programs; the

overall structure of the system has been shown in

figure.2.

http://en.wikipedia.org/wiki/Safety_engineering

http://en.wikipedia.org/wiki/Reliability_engineering

http://en.wikipedia.org/wiki/Reliability_engineering

225 K. RAM CHANDAR AND H. AGARWAL



Fig2: Structure of ES-VENT system (L.K.

Bandyopadhyay and S.K. Sinha, 2002)

3.2. The mine ventilation manager (MVM):

Mine ventilation manager (MVM) is a type of expert

system which solves a lot of ventilation problems like it

detects drop in airflow and makes suggestions in

response, it can check for device failures and methane

build-up. In addition, MVM also detects and provides

consultations about potential or actual fire emergencies.

MVM is a sort of multifunctional expert ventilation

system as it tries to integrate certain domains of mine

ventilation expertise into one system and simultaneously

a user is provided with a consultation on any problem or

combination of problems that arise in a mine (fig. 3).

Important features of MVM (Altman, Hughes, and

Wala, 1988) are:

1. It has the ability to determine optimal flow

distribution (with respect to the amount of energy

required to satisfy environmental constraints).

2. It provides a means to verify a mine model, either

proposed or assumed, for application of normal

ventilation strategies.

3. It determines the settings for the regulators to

accommodate a new airflow distribution.

4. It includes software to perform critical-path

airflow calculations, from which the required

regulator settings can be computed.

Fig3: General structure of MVM (Altman, Hughes and

Wala, 1988)

4. Development of expert system on 3D stope

stability assessment:

This is another example of expert system in mining,

Stopes are the openings made in the process of

extracting ore, and these are also called rooms. Two

steps are involved in stoping.

1. Development-It includes preparing the ore blocks

for mining.

2. Production-It includes stoping.

A new expert system (ES) called stope stability

assessment program (SSAP) has been developed with

the help of Canada Centre for Mineral and Energy

Technology (CANMET) by Vongpaisal, et, al., (2011)

for underground blast hole mining operations with

delayed backfill, at depth from 0.5 km to 2km below the

ground surface.

This program mimics the rational processes of rock

mechanics experts to provide recommendations to mine

operators which enables them to make decisions on

strategic mine planning and feasibility studies. Due to

this, planning and decision-making can be done in a

quick and efficient manner to minimize the risk of

ground failure and can optimize mining costs also. It

helps in managing the risk involved with high

productivity.

The geo-mechanics/ rock mass classification system, or

rock mass rating (RMR) in both the hanging wall and

foot wall is assumed to be ≥60 for the development of

this expert system, This assumption represents general

ground conditions of hard rock mining in Canada. Rock

mass properties and pre-mining stress regimes have to

be assigned.




4.1. Creation of logic blocks for development of SSAP:

For the simplification of maintenance and to easily

modify software’s a set of rules (IF/THEN rule) have

been formulated in different logic blocks by this system

developers. Each logic block consists of specific aspects

of the decision-making rules and tasks. These include:

1. Start-logic block

2. Rock strength logic block

3. Rock bolt logic block

4. Get damage zone block

5. Get grid nodes logic block

6. Show chart logic block

Start-logic block – It contains rules for making

decisions on rock mass classifications.

Rock strength logic block – It determines whether

the stope is located in a good hard ground condition

according to assumed values of RMR. Heuristic rules

have been set for the allowable maximum limit of the

potential extent of the damage zone (α).

Get damage zone block – In this module,

interpolation equations were applied from ‘Get Grid

Nodes Logic Block. It determines damage zones

values.

Get grid nodes logic block – It assigns node values

of H/W, L/W and DZ of particular stope widths at

particular depths. The data is input into knowledge

bases, using the XML format in order to improve the

efficiency of determining DZ at particular stope

dimensions.

Show chart logic block – It displays potential extents

of damage zone surfaces and associated dilutions in

3D spaces, if requested by user.

Rock bolt logic block – It contains rules on ground

support requirements

4.2. Creation of command blocks for development of

SSAP:

The command blocks are those important parts of expert

system which instruct the expert system on how to

proceed. In this module, command blocks tell the

system to determine DZ (damage zones) at any

particular point from database grid nodes blocks, derive

confidence variables and display results.

5. Expert system CMEOC:

Coal mining expert and optimization consultation

system (CMEOC) (Hong Zhang, Guanghui Zhao, 1999)

developed a coal mining engineering expert system with

optimization techniques to reach the goal of optimal

decision making.

5.1. Architecture of CMEOC expert system:

It is mainly designed for coal mining. It is used to

determine underground mining methods, open-pit

mining and transportation systems, etc. In this according

to Zhang and Zhao, (1999), first of all an expert system

is used then optimization techniques such as multi

objective (MO) programming, fuzzy sets (FS), integer

programming (IP), etc. are used to generate the final

recommendation (fig. 4).

The system consists of three components (Hong Zhang

and Guanghui Zhao, 1999): -

1. An expert system.

2. Optimization techniques.

3. A design and drawing (DD) system.

Fig4: Structure of CMEOC (Hong Zhang, Guanghui

Zhao, 1999)

Expert system:

The expert system consists of a knowledge base (KB),

an inference system (IS), and a control system (CS).

Knowledge base:

The knowledge base consists of three components.

The static database is used mainly to store the

data of the current intermediate information

acquired in the inferencing process.

The rule base represents the expert knowledge.

Theoretical knowledge of special fields such as

mining engineering.

There are three kinds of representation of knowledge in

the system.

1. Representation of facts: Predicate calculus is

one of the methods used to represent knowledge.

2. Representation of reasoning knowledge: In the

system production rules are used to represent

reasoning knowledge (Zhang, 1988). It generally

uses ‘IF’ and ‘THEN’ commands.




3. Representation of fuzzy knowledge: Experts’

knowledge can be ambiguous and imprecise.

Expert knowledge can be assigned a reliability

factor b (0 < b <1). There are three ways to

determine the value of b. They are: mathematical

method, expert judgment method and similarity

comparison method.

The IS and CS consist of a set of programs which

controls and coordinates the whole system (Wu, 1991).

IS acts as the key of the expert system. It solves the

problem according to certain inference and control

strategies using the knowledge base. They work like

this: the user provides inputs and the related facts are

stored in the knowledge base through CS and IS. Based

on the initial inference results, the relevant optimization

technique module is invoked through IS, and then the

final optimal recommendation is made.

Optimization techniques:

One can get a set of initial solutions such as Ai (i= ¼ 1,

2, 3, ¼, n) on using expert systems like CMEOC. But

the user only wants an optimum solution, so it is

necessary to find an optimal solution on the basis of

initial solutions. IS uses the solutions obtained from the

inference process as the intermediate solution and

passes that to the intermediate scheme database M1,

M2, ¼, Mn. Then IS invokes the relevant optimization

techniques to make the final recommendation.

6. An expert for hydraulic excavator and truck

selection in surface mining:

This system was developed by Kirmanli and Ercelebi, in

2009. The main purpose of this expert system is to

choose the most appropriate configuration of hydraulic

excavator and truck so that unit production cost is

minimized and technical constraints such as geological,

geotechnical and mining constraints are satisfied. This

ES consist of four modules (fig. 5) (Kirmanli and

Ercelebi, 2009):

1. User interface.

2. Rules database.

3. Methods database.

4. Output module.

This type of ES is developed within Kappa PC shell. It

also supports object-orientated technology for the MS

Windows environment (C. Kirmanli and S.G. Ercelebi,

2009).

It acts as a very useful tool to practitioners by saving

time and cost. This type of expert system overcomes the

difficulties of selecting the proper equipment for surface

mining operations, which is very important, and results

in tremendous savings. First of all, equipment databases

are made for hydraulic excavators and trucks with

different capacities and then these databases are used to

select proper configuration.

Fig5: Hydraulic excavator–truck selection expert

system structure (Kirmanli and Ercelebi, 2009)

In this system ‘IF’ and ‘THEN’ format have been used

to construct Production Rules, and a new rule can be

added whenever it is needed.

Methods which are formed with more than one rule are

widely used in expert systems to reduce complexity and

working time.

This type of expert system has two main databases

(Kirmanli and Ercelebi, 2009):

1. The hydraulic excavator database.

2. Truck database.

With the aid of the output module, the results are

displayed on the screen and can either be printed or

saved in a file.

6.1. Equipment selection criteria:

This criteria is important for the selection of hydraulic

excavators and trucks. It is divided in to six classes:

1. Diggability.

2. Production criteria

3. Mine parameters

4. Geological and geotechnical factors

5. Equipment criteria

6. Unit production cost.

Diggability: The main parameters of the dig-ability

classification system are:

1. Uniaxial compressive strength (UCS).

2. Seismic velocity.

3. Degree of weathering.

4. The characteristics of joint sets.

5. Thickness of formation.




Examples of certain diggability classification systems

which have been developed by several researchers are:

1. Franklin (…..) developed a classification system

based on a graphical method, by using rock

strength, discontinuity spacing and point load

strength. A graph is divided into four areas and

the areas are defined as digging, ripping, blasting

for loosening and blasting for breaking.

2. Atkinson (…..) developed another classification

system, which depends only on seismic velocity.

It categorizes equipment according to their

digging performance.

6.2. Excavator-truck expert system architecture:

In this expert system the very first step is to find out

about the dig-ability. In order to determine this one need

to enter values of UCS, degree of weathering,, seismic

velocity, average discontinuity spacing and bedding

thickness. These values are entered in this system with

the help of Diggability assessment input screen.

Material and coal density data are also given in this

screen. A flow chart of this expert system is given in

fig. 6.

Diggability criteria are determined according to

information supplied by the user. The user is asked for

the elasticity modulus and Poisson’s ratio if seismic

velocity is unknown and then it calculates the seismic

velocity by using these values and compressive strength.

Production and mine parameter information is given to

the expert system on the mining section screen. For

excavator and truck selection calculations and to run

related production rules sometimes it becomes

mandatory to input information related to mine

parameters, such as annual production, bench height,

etc.

The expert system determines the mine life after

production information is supplied, to it and it also

calculates annual required waste production by using

the stripping ratio. The next step is to give geotechnical

criteria to the expert system from the material section

screen. In this section, blasting conditions for the waste

and mineral and average size distribution of blasted

material are also supplied by the user. The elasticity

modulus and Poisson’s ratio values, which are assigned

to some default values, are not used when the seismic

velocity is given at the beginning of interrogation.

Fig6: Architecture of excavator-truck ES (Kirmanli and

Ercelebi, 2009)

7. Conclusions:

This paper summarises some of the expert systems

developed by various researchers for different

applications for mining industry. Such expert systems

are very useful in managing the mining activities more

effectively. Some of these systems can be modified to

suit the need requirements. To meet the ever increasing

demand for natural resources, such expert systems to be

deployed to achieve the required targets and to improve

the productivity by maintaining higher standards of

safety.

8. Reference:

[1] A.I. Koketayev, Using of Expert Systems in

Ventilation Systems Controlling, lä" International

Mining Congress and Exhibition of Turkey-IMCET

2003, pp.263-264.

[2] C. Kirmanli and S.G. Ercelebi, An expert system

for hydraulic excavator and truck selection in

surface mining, The Journal of The Southern




African Institute of Mining and Metallurgy,

Volume 109,pp. 727-737

[3] Hong Zhang and Guanghui Zhao, CMEOC—An

expert system in the coal mining industry, Journal

of China University of Mining and Technology,

Xuzhou 221008, People’s Republic of China vol.

16, pp. 73-77

[4] Huang Xin, An integrated decision support system

for Backfill design, Thesis, 1994.

[5] K. Sarkar, V. Vishal and T N Singh, 2012, an

empericla correlation of index geomechanical

properties with the compressional wave velocity,

Geotc. Geol. Engg., 469-479.

[6] S. Vongpaisal, G. Li, R. Pakalnis & T. Brady, New

development of expert system module for a

decision-making on mine stope stability in

underground blastholemining operations,

International Journal of Mining, Reclamation and

Environment,pp.41-51

[7] R. Trivedi, V. Vishal, S. P. Pradhan, T. N. Singh, J.

C. Jhanwar, 2012, Slope stability analysis in

limestone mines, International Journal of Earth

Sciences and Engineering, 5(4): 759-766.

[8] V. Vishal, S. P. Pradhan and T N Singh, 2010,

Instability Assessment of Mine slope- A finite

element approach, International Journal of Earth

Sciences & Enggineering, 3:11-23.

[9] V. R. Sastry and K. Ram Chandar, 2008.

Assessment of blast performance based on energy

distribution: Proc. 42nd

American Rock Mechanics

Association Conference, San Francisco, USA, 29th

June-02nd

July- 2008.

[10] V. R. Sastry, V. R and K. Ram Chandar, 2010.

Stability analysis of highwall- case study of an

opencast coal mining project. Mining Engineers

Journal, Vol-12, No.4, Nov-2010, 18-24.

[11] K. Ram Chandar, T.N Singh and P. Ravi Kiran, P.,

2001. A computational approach for prediction of

rock fragmentation, Mining Engineers Journal,

July-2001, 16-25.

[12] K. Ram Chandar, K. 2002. Computer aided design

of hydraulic stowing. Coal Mining Technology &

Management, March-2002, 1-8.

[13] K. Ram Chandar and T. N Singh, 2002. Computer

aided roof load estimation for bord & pillar

workings, Mineral Industry: Issues on Economics,

Environment and Technology-2002, 65-74.

[14] Zhang Y. 1988. An expert system for strip mining

under the buildings, Journal of China University of

Mining and Technology, vol.4, pp. 44–50.

www.cafetinnova.org

Indexed in





ISSN 0974-5904, Volume 07, No. 01

February 2014, P.P.230-238


Interpretation of Depositional Environment of Miocene Sequence

Using Electrofacies Analysis in the Well Bakhrabad # 09, Bengal

Basin

ABU REZA MD. TOWFIQUL ISLAM1, MD. AMINUL ISLAM

2, MD. EMDADUL HAQUE

1 AND

KHURSHIDA JAHAN3

1Department of Disaster Management, Begum Rokeya University, Rangpur-5400, Bangladesh

2Department of Petroleum Geoscience, University Brunie Darussalam, Gadong BE-1410, Brunie

3Department of Chemistry, Jahangirnagar University, Savar, Dhaka-1342, Bangladesh

Email: [email protected].

Abstract: Wireline log and limited core samples data were integrated to used in order to reconstruct paleo

environment of deposition of Miocene sequence in the well Bakhrabad-09, Marichakandi structure, Bengal Basin.

The main aim of this study was to interpret the depositional environment of the Miocene sedimentary sequence

using electrofacies analysis. Miocene sequence was subdivided into two sequences that consist of 7 first-order

cycles and 33 second-order cycles in the study area. The identified electrofacies were bell, funnel and egg/bow,

linear and cylindrical shaped etc. in nature. The environment of the Upper Bhuban sequence-2 (UBS-2) inferred to

be deposited (2955 to 2280 m) under lower deltaic plain to marginal marine setting while the Boka Bil sequence-1

(BBS-1) presumed to be deposited (2280 to 799 m) under fluvio-deltaic setting to shallow marine environment. The

study revealed that both deltaic progressive and retrogressive phases occurred more frequently during the deposition

of both sequences (UBS2-BBS1) but whole nature of eletrofacies shows coarsening upward deltaic progradation.

Keywords: Miocene sequence, Depositional environment, Electrofacies analysis and Deltaic progradation.

1. Introduction:

The term “electrofacies” was first introduced by Serra

and Abbott [1] that assigned to one or more lithofacies

as wireline log responses are interpreted in the

subsurface sedimentary environment. Detailed seismic,

geological and geophysical studies were established in

the Morichakandi structure for discovering Meghna gas

field of Bangladesh [2]. Various studies were carried

out for interpretation of depositional environment of

sedimentary sequence of Bengal Basin [3-11]. The

Bengal Basin has received special attention by earlier

workers largely due to its commercial feasibility for

hydrocarbon prospects. So far, there are no detailed

studies done for the well Bakhrabad-09 in the Bengal

Basin by integrating electrofacies with core sample.

In this regard, the research work an attempt has been

made by well log data and limited core to interpret the

depositional environment of the Miocene sequence in

the well Bakhrabad-09. The outcome of the study is to

analyses the electrofacies of wireline log motif and core

sample in order to identify in detail electofacies,

sequences, cycles and associations for interpretation of

depositional environment of the well Bakhrabad-09.

Electrofacies can provide information on the lithology

and sequences of the rock as well as depositional history

[8]. Electrofacies analysis shows the well log

characteristics such as base line, log shapes or motif,

abrupt changes etc. GR or SP log base line is constant

either maximum or minimum values both lithologic and

stratigraphic importance [13]. Log shape with thickness

are correlated to sedimentary facies cycles, sequences,

associations indicates the basin fill history of large and

small events. GR log shaped could be interpreted as

grain size trend [8]. The study area is situated in the

district of Brahmanbaria which is bounded in the

northeast by Sylhet Trough and in the southeast by

Chitagong-Tripura Folded Belt and in the west by Hinge

zone and open to the south and southeast to the main

part of Bengal Basin (Figure 1).

231 ABU REZA MD. TOWFIQUL ISLAM, MD. AMINUL ISLAM, MD. EMDADUL HAQUE AND

KHURSHIDA JAHAN



Fig1: Location map showing the study well Bakhrabad-09 in the Marichakandi structure.

2. Geology of the structure:

The Bengal Basin of Bangladesh is a remnant ocean

basin in the world [8]. The Greater Bakhrabad structure

lies on the southern fringes of Bengal Basin. Greater

Bakhrabad structure is an elongated close anticline and

is about 67 km long and 6 km wide [2]. Morichakandi

Structure is a sub-structure of the greater Bakhrabad,

which lies on the north western part of the Bakhrabad. It

is located in the crestal region of Bakhrabad anticline

complex. The structure is the larger than that of other

Bakhrabad and Belabo structures forming the greater

Bakhrabad anticlinal complex. Geologically,

Morichakandi Structure is situated in the western most

part of the Chittagong-Tripura folded belt which lies on

the north western part of the Bakhrabad. Titas Structure

is present in the north and Kamta Structure lies in the

west. Morichakandi Structure is a symmetrical anticline

with SE-NNE [12]. The structure perhaps started

intensifying during Early Miocene and its apex

development took place probably Late Miocene

sedimentation and finished in Pliocene time. The sub-

surface stratigraphy of the study area was established on

the basis of drilling data, log data, seismic data and also

correlation with neighboring established well [12].

Stratigraphy of the structure is presented in the table 1.

Table1: Stratigraphic succession of the study area (After BOGMC, [12])

Age Group Formation/Sequence (m) Lithological description/Characteristics

Recent Alluvium (61 m) Dominantly loose sand, fine to medium grained sand and

clay

Pliocene

Dupi

Tila Dupi Tila (110 m) Mainly sandstone with interrelation of shale

Miocene

Tipam Tipam Sandstone (628 m) Mostly grey quartz sand, medium to fine-grained with some

sticky clay and intermediate of silt.

Surma

Boka Bil sequence-1 (1481

m)

Mainly dark grey thick clay and interbedded with sandstone

and alternate sand and silt. Medium to fine-grained, sub-

angular and calcareous cementing material show cross

bedding structure of sandstone.

Upper Bhuban sequence-2

(675 m)

Predominantly light grey sandstone, medium to very fine-

grained, sub-angular to rounded, calcareous cement,

interbedded with shale with siltstone, slightly calcareous

and argillaceous cementing material.

232 Interpretation of Depositional Environment of Miocene Sequence Using Electrofacies




3. Materials and Method:

The different wireline log data especially GR and SP

has been collected from the Data Centre, Petrobangla.

The limited core sample was also collected from

Bangladesh Petroleum Exploration & Production

Company Limited (BAPEX). The methodology for log

interpretation in this study is adopted after Rider [13]

and Serra [14]. GR and SP logs have been presented in

a composite mode after sensitivity matching to generate

visual facies or log motif treated as electrofacies. The

focus of the present study has been given on the Gamma

Ray (GR) log to analyze the electrofacies, cycles,

sequences and association for interpretation of

depositional environment of the Miocene sequence.

Hard copy of different logs has been transferred

manually to digital format maintaining the limit of

optimum resolution. Later on, the digital data has been

regenerated as an analog curve of different logs using

MS-Excel software. The digitization and regeneration of

GR log motifs have been done in a optimum scale at the

study well Bakhrabad-09 to determine the lithological

variation and major change in lithology which give the

clue of depositional history. Limited core sample has

been used to interpret depositional environment (Figure

2).

Fig2: Methodology adopted for the electrofacies analysis of the study

4. Results and Discussions:

Electrofacies is an individual set of log responses that

are characteristics for a particular lithology usually

necessary to calibrate logging data with core

information from key intervals [15]. Various authors

made valuable contribution in the field of sedimentary

geology of sub-surface sedimentary sequences by the

exploratory study of wireline log responses [16-26]. GR

log shapes or motifs can provide clue to the different

sub-environments of deposition of the sequences. First-

order cycles (para-sequence-sets) are composed of bed

sets i.e. facies associations and second-order cycles

(para-sequences) are composed of lamina set beds i.e.

facies/electrofacies within the sequences [27]. The

coarsening upward sequence is progradational and

fining upward sequence is retrogradational sequence

with rare occurrence of aggradational sequence [28].

4.1. Interpretation of electrofacies and depositional

environments:

Electrofacies has been subdivided into five types which

identified in the study on the basis of GR and SP log

motifs or shapes described briefly as follows:

4.1.1 Bell shaped electrofacies: Bell shaped

electrofacies indicate a fining upward sequence where

GR log value and shale content increase upward and

sand content decrease upward. The hydrodynamic

condition decrease upward at the time of deposition

which represents retrograding distributary channel

floodplain, interdistributary channel, mud fiat and

fluvial channel floodplain sub-environment. The typical

bell and serrated bell shapes of the log motifs in the

study well are observed at various depth intervals in

both sequences (Figure 3a and 3b).

4.1.2 Egg/Bow Shaped electrofacies: Egg shaped

electrofacies show both coarsening and fining upward

or vice versa sequences with equal sand/shale ratio

suggests aggradational environment in channel-

floodplain, sub-tidal flat, mud flat and prograding-

retrograding of mud rich fan system. The egg/bow

shape is observed at various depths in the study well

(Figure 3c).

4.1.3 Cylindrical shaped electrofacies: Cylindrical

shaped electrofacies indicate a thick homogeneous

sediment constrained by channel-fill deposit with sand


KHURSHIDA JAHAN



dominating sequence where GR log value suggested

that more or less uniform sequence and the depositional

environment such as aggrading fluvial channel and

distributary channel and tidal sand flat. The cylindrical

shaped and serrated cylindrical shaped are shown at

various depths (Figure 3d).

4.1.4 Linear shaped electrofacies: Linear shaped

electrofacies are generally steady shape and shale

dominating sequence suggests uniform depositional

sequence either of coarse grained or fine grained related

to deposition of inter-distributary bay to shallow marine

condition. The linear shaped is identified at the depth

range of 799-934 m in the study well (Figure 3e).

4.1.5 Funnel shaped electrofacies: Funnel shaped

eletrofacies reflect a coarsening upward sequence where

GR log value and shale content decrease upward and

sand content increase at upper part. The hydrodynamic

condition increases upward at the time of deposition as

in the case of prograding delta, alluvial fan and

regressive shallow marine bar environment. The funnel

shape and serrated funnel shaped are identified in the

Miocene sequence at different depths in the study area

(Figure 3f).

Fig3: Typical Gamma Ray (GR) log shapes of the Miocene sequence in the well Bakhrabad-09.

In depth interval from 2955 to 799 m, a detailed study

from the well base to upper part is done on the basis of

Gamma Roy (GR) log shapes or motifs and trends.

Miocene sedimentary sequence is characterized by first-

order and second-order cycles based electrofacies

analysis and core sample consisting of two sequences

(BBS 1 and UBS 2) are described below:

4.2. Interpretation of the Upper Bhuban Sequence-2

(UBS-2):

The Upper Bhuban Sequence-2 (UBS-2) of Miocene

sequence is present in the depth interval from 2955 to

2280 m. This sequence is divided into 2 first-order

cycles and 12 second-order cycles which were identified

based on the relationship of GR log shapes, grain sizes

and change in log motifs.

4.2.1 First-order Cycle-1 (BHC-1): First order cycle is

identified within the depth range of 2955-2665 m

having a thickness of 290 m, shows fining upward (Fu)

sequences with few fluctuations and decrease in the

grain size towards top of the sequences (Figure 4). Silty

sandstone lithofacies and shaley lithofacies having

relative proportion of 35% and 65% respectively. Shale

dominating electrofacies increases upward of the cycle

indicating decrease of hydrodynamic condition. The

first-order cycle-1 consists of 2 coarsening upward (Cu)

sequences at bottom part, 4 Fu sequences at top most

part and 1 homogeneous sequence at middle part of the

cycle have been identified in this sequence. Serrated

bell, linear and serrated funnel shaped electrofacies

suggests different sub-environments i.e retrograding

distributary channel, shallow marine, interdistributary





bay and prograding channel sub-environments of

deposition.

4.2.2 First-order Cycle-2 (BHC-2): This cycle is

present within the depth ranges of 2665-2280 m having

thickness of 385 m shows coarsening upward sequence

with minor fluctuations and increase in grain size

upward (Figure 4). Silty shaly lithofacies constitutes

about 62%, whereas sandstone facies remain at the

lower part of the cycle. 3 second-order Fu sequences at

the top upper part of the cycle and 4 Cu sequences

present at the bottom part of the cycle. Serrated

cylindrical, serrated funnel and bell shaped electrofacies

represent different sub-environments i.e. aggrading

distributary channel, prograding channel, retrograding

channel and tidal mudflat sub-environments. Overall the

first-order cycle-2, indicates prevalence of deltaic

progradation with marine regression conditions.

The core of the lower part of the sequence consists of

thin lenticular bedded shaly facies with sandstone

showing very fine to medium grained and cross

stratified indicating deltaic retrogressive phase of

depositional environment (Figure 5a). The core of upper

part of this sequence consists of ripple cross-laminated

wavy bedded sandstone facies; thin stratified shale

indicates deltaic progradation with marine regressive

phase of depositional environment (Figure 5b). The

Upper Bhuban Sequence-2 (UBS-2) consists of 3

phases of prograding channel and 2 phase of

retrograding distributary channel and 2 phases of

aggrading distributary channel and tidal channel, sub-

tidal mudflat, tidal sand flat and inter-distributaries

representing bay to shallow marine condition (Figure 4).

Fig4: Gamma Ray (GR) log responses, first-order cycles, log shapes and possible environments of deposition of the

Upper Bhuban Sequence-2 in the well Bakhrabad-09.


KHURSHIDA JAHAN



Fig5: Core sample photograph show lithofacies of the study area: a) flaser bedded facies; b) wavy bedded facies; c)

lenticular bedded facies. Sand shows dark grey color and shale shows light grey color (After BAPEX, [2]).

4.3. Interpretation of the Boka Bil Sequence-1 (BBS-

1):

The Boka Bil Sequence-1 is the uppermost part in the

depth range of 2280 to 799 m. This sequence comprises

5 first-order cycles/para-sequence sets and 21 second-

order cycles/para-sequences were identified as follows:

4.3.1 First-order Cycle-1 (BBC-1): This cycle is

identified within the depth ranges of 2280-1975 m

having thickness of 305 m, shows fining upward

sequences with few fluctuations towards the bottom part

(Figure 6). Sandstone and silty shaly facies cover about

40% and 60% respectively. Lower to middle part of the

cycle (2286-2099 m) indicates relatively higher

hydrodynamic condition which gradually decreases

towards the upper part. 2 Cu upward sequences

identified at the lower and middle part and 3 Fu upward

sequences at the top part. Serrated funnel, serrated bell,

asymmetric cylindrical and linear shaped electrofacies

suggests prograding delta, retrograding distributary

channel and inter-distributary bay etc. The overall

deposition occurred under deltaic distributary channel

and inter-distributary bay conditions.

4.3.2 First-order Cycle-2 (BBC-2): This cycle is

present within the depth range of 1995-1689 m (306 m)

and shows almost fining upward sequences (Figure 6).

This set covers sandstone, silty shale and shale facies at

about 50%, 30% and 20% respectively. Cylindrical,

serrated funnel, and bell shaped electrofacies indicates

retrograding distributary channel during the deposition

of this cycle.

4.3.3 First-order Cycle-3 (BBC-3): The cycle covers

the depth range of 1689-1304 m (385 m), shows

decreasing GR value representing coarsening upward

sequences which indicate increase in grain size towards

top (Figure 6). Sandstone, silty shale and shale facies

constitute about 45%, 20% and 35% respectively.

Identified linear, funnel and serrated bell shaped

electrofacies suggests tidal floodplain complex,

prograding channel, tidal channel and inter-distributary

bay sub-environments. This cycle represents at least

three phases of progradation, three phases of

retrograding distributary channel and one phase of inter-

fluvial deposit. The overall deposition occurred under

marine regression conditions.

4.3.4 First-order Cycle-4 (BBC-4): This First-order

cycle present within the depth range of 1304-934 m

(470 m), shows coarsening upward sequences (Figure

6). Sandstone and shale facies constitute about 60%

about 40% respectively. Sandstone facies indicating

coarsening upward sequence suggests higher energy

condition whereas shaley facies suggests gradual

decrease of the hydrodynamic condition. This cycle

contains 3 Cu sequences and linear, bell and serrated

funnel shaped electorfacies represents inter-distributary

bay, retrograding channel and prograding channel

floodplain complex. Lower part of the cycle indicates

retrograding deltaic phase but upper part of the cycle

indicates prograding deltaic phase of deposition.

4.3.5 First-order Cycle-5 (BBC-5): The cycle is

identified within the depth range of 934-799 m (135 m),

shows decreasing GR value indicating fining upward

sequences towards top (Figure 6). Shale facies

constitutes about 100% indicating calm and quite

energy condition. Linear shaped electrofacies represents

shallow marine conditions, regarded as "Upper Marine

Shale" is interpreted as the last phase of marine

transgression [29].

The core in the lower part of the sequence-1 (BBS-1)

consists of light to dark grey sandstone with thick

stratified shale and characterized by flaser bedding.

Middle part of the sequence is wevy bedded, consists of

ripple cross-laminated sandstone, very fine to fine

grained, calcareous with coal fragments and sparse





bioturbation (Figure 5a and 5b). Upper part of the

sequence is lenticular bedded, consists of dark grey thin

laminated shale with silty sandstone (Figure 5c). Core

sample study suggests that two marine regressions and

two transgressions of deposition occurred repeatedly

within this sequence. The Boka Bil Sequence-1 (BBS-1)

contains 3 phases of retrograding distributary channel, 2

phases of aggrading channel and 2 phases of prograding

channel with tidal channel, tidalflat, inter-distributary

bay to shallow marine regime have been identified in in

this sequence(Figure 6).

Fig6: Gamma Ray (GR) log responses, first-order cycles, log shapes and possible environments of deposition of the

Boka Bil Sequence-1 in the well Bakhrabad-09.

4.4. Interpretation of depositional environment:

On the basis of core study and electrofacies, 7 first-

order cycles and 33 second-order cycles were identified

in the study area. All the sequences are either Fu or Cu

sequences with cylindrical, bell, funnel, linear and

egg/bow shaped etc. in nature. 2 first-order cycles and

12 second-order cycles were identified in the Upper

Bhuban Sequence-2 (UBS-2). The sequence with depth

range of 2265-2955 m consists of 3 Fu upward and 2 Cu

upward sequences with funnel, serrated bell, cylindrical

and serrated linear shaped electrofacies suggests a phase

of marine transgression and then slightly deltaic

progradation during the deposition of the cycle. The

depth range of 2280-2665 m consist of 4 Cu upward

sequences at the bottom part and middle part and 3 Fu

upward sequences at the top part of the cycle suggests a

phase of deltaic progradation and then retrogressive


KHURSHIDA JAHAN



deltaic phase during the deposition of the cycle.

Serrated funnel shaped electrofacies represents siltstone

to sandstone facies indicating prograding deltaic

environment but bell shaped electrofacies shows silty

shale to shaley facies suggests retrograding deltaic

environment. So, the Upper Bhuban Sequence-2 (UBS-

2) assumed to be deposited in lower deltaic setting to

marginal marine condition during basin subsidence,

high rate of sediment supply and autocyclic migration of

different sub-environments of deposition.

5 first-order cycles and 21 second-order cycles were

identified in the Boka Bil Sequence-1 (BBS-1). The

depth interval of 1975-2280 m show Fu sequence with

serrated bell, funnel, and cylindrical shaped eletrofacies

suggests retrograding channel and tidal channel,

prograding-aggrading channel sub-environments of

deposition. The depth ranges of 1689-1975 m show Fu

sequence with serrated funnel shaped electrofacies and

wavy bedded facies indicate tidal sand flat and also bell

shaped electrofacies and flaser bedded facies show

retrograding distributary channel. The sequence with

depth ranges of 1304-1689 m consist of Cu upward with

funnel, bell and serrated linear shaped electrofacies

suggests prograding, retrograding and inter-tidal

mudflat sub-environments. The Cu upward sequence

represents by linear, bell, and funnel shaped

electrofacies indicating interfluvial, retrograding

channel and prograding channel sub-environments from

depth interval of 934-1304 m. Finally, the depth ranges

from 799-934 m was “Upper Marine Shale” shows

linear shaped electrofacies with thick shaley facies

suggests shallow marine conditions. Also made similar

observation made by Mondol et el. [8] in Shahbazapur

structure of Bengal Basin. The Boka Bil sequence-1

(BBS-1) supposed to be deposited under fluvio-deltaic

setting to shallow marine conditions in response to a

marine transgression and regression phase during the

deposition of the sequences. It was interpreted that some

cyclic phases of marine transgression with regression

were occurred during the deposition of the Miocene

sequence in the study. BAPEX [2] also confirmed

similar result by seismic interpretation of the

Marichakandi structure in the well Bakhrabad-09.

5. Conclusions:

The well Bakhrabad-09 drilled in Marichakandi

structure contains well developed sedimentary

sequences from the Miocene to recent age. The Upper

Bhuban Sequence-2 (UBS-2) identified 2 first-order

cycles with sandstone lithofacies, and bell, funnel and

egg/bow shaped electrofacies indicate marine regression

with deltaic progradation phase. The top most part of

the Boka Bil Sequence-1 (BBS-1) identified 5 first-

order cycles with linear shaped electrofacies and shale

lithofacies suggests marine transgression. The

Marichakandi structure of Miocene sequence might

have been deposited under lower deltaic plain to

marginal-marine and fluvio-deltaic setting in response

to marine regression and transgression, basin subsidence

and increase of sedimentation of different sub-

environments. Overall nature of the log trend shown

that lower delta plain was deposited at base and deltaic

progradation with marine regressive phase occurred at

upper part of the Upper Bhuban Sequence-2 (BHS-2).

The general nature of the log shape indicated that

deltaic retrogressive phase was deposited at the lower

part and then deltaic progradation phase deposited at

middle part and finally marine transgression phase

occurred at top part of the Boka Bil Sequence-1 (BBS-

1). It reveals that deltaic prograding sequences are

sandstone dominating reservoir rock whereas deltaic

retrograding sequences are shale dominating rock within

both sequences. The study suggests that multiple

episodes of marine transgression and regression regime

occurred during deposition of the Miocene sequence.


The authors sincerely thank the reviewers especially Dr.

AN Reddy Chief Geologist (Retd), Oil and Natural Gas

Corporation, Channei, India for his critically reviewing

this manuscript and suggestions to improve the quality

of the paper. We wish to thanks Prof. D. Venkat Reddy

Editor in Chief of International Journal of Earth Science

& Engineering for his valuable comments to revise this

paper. We would like to grateful to the Chairman of

Bangladesh Oil, Gas and Mineral Corporation

(Petrobangla) and the Managing Director, BAPEX for

their kind permission to access data for well log and lab

support & facilities for core sample analysis. Also

thanks are due to Prof. Sultan-ul-Islam; University of

Rajshahi made valuable comments and suggestion while

conducting this research work.

7. Reference:

[1] O. Serra and H. T. Abbot, The contribution of

logging data to sedimentology and stratigraphy,

55th Annual Fall Meeting of AIME (SPE 9270),

Dallas, Texas, 1980.

[2] BAPEX, Interpretation Report on the greater

Bakhrabad Structure, Dhaka, 1989.

[3] D. N. Sultana and M. M. Alam, “Facies analysis of

the Neogene Surma Group succession in the sub-

surface of the Sylhet Trough, Bengal Basin,

Bangladesh” Bangladesh Geoscience Journal, v. 6,

p. 53-74, 2000.

[4] F. Deeba, D. Hossain and A. Q. M. R. Rahman,

“Geology and hydrocarbon potentiality of Beani

Bazar Structure.in Surma Basin Bangladesh using

geophysical and well data” Bangladesh

Geoseciences Journal, v. 7, 2001.





[5] M. M. Alam, J. R. Curray, M. L. R. Chowdhury

and M. R. Gani, “An overview of the sedimentary

geology of the Bengal Basin in relation to the

regional tectonic framework and basin fill history”

Sedimentary Geology, v. 155, p. 179-208, 2003.

[6] M. M. Hossain, N. E. Huq and M. M. Huq,

“Depositional environment of the Neogene clastic

succession (Surma Group) of the Kailas Tila

Structure in the Surma Basin, Bangladesh”

Bangladesh Geosci. Jour. v. 9, p. 47, 2003.

[7] C. Devices, J. Best and R. Collier, “Sedimentology

of the Bengal Shelf, Bangladesh; Comparison of

late Miocene sediment, Sitakund anticline, with the

Morden, tidally dominated shelf” Sedimentary

Geology, v.155 (3, 4), p.271-300, 2003

[8] D. Mondal, M. S. Islam and A. Islam, “

Electrofacies analysis of Neogene sequence in the

well Shahbazpur #1, Bhola, Bengal Basin” ICFAI,

Journal of earth Science, v.3 (1), p.57-74, 2009.

[9] J. J. M. Rahman, M. M. Alam and P. Faupl,

“Depositional facies of the subsurface Neogene

Surma Group in the Sylhet trough of the Bengal

Basin, Bangladesh: record of tidal sedimentation”

International Journal of Earth Science, v.98 (8),

p.1971-1980, 2009.

[10] S. W. Gomes, M. M. Alam, A. Uddin, and S. W.

Wise, “Depositional pattern of Deep Marine

Neogene Surma sequence in the Sitapahar

Anticline, Chittagong Hill Tract, southeastern

Bengal Basin”, GSA Denver annual meeting, Geol.

Socie. of Ame., Colorado, USA, abst, v.42 (5),

p.429, 2010.

[11] M. A. Islam, M, “Depositional environment of

Neogene reservoir succession of Bengal Basin,

Bangaldesh constrain from lithofacies and

electrofacies analysis”, Geophysical research

abstracts, EGU General Assembly, v.14, p.727,

2012.

[12] BOGMC, Well Completion Report, Bakhrabad # 9

Geological Evaluation Division. Petrobangla,

Dhaka, 1990.

[13] M. Rider, Geological Interpretation of Well Logs:

Whittles Publishing Services, 1999.

[14] O. Serra, Fundamentals of Well-Log Interpretation

(Vol. 2): The Interpretation of Logging Data. Dev.

Pet. Sci., 15B, 1986.

[15] H. G. Reading, Sedimentary Environments and

Facies, Blackwell Scientific Pub. Oxford, 1978.

[16] W. E. Galloway, Depositional Systems of the

Lower Wilcox Group, North Central Gulf Coast

Basin, Gulf Coast Association Geol. Soc. Trans.

v.18, p. 275-289, 1968.

[17] W. C. Krueger, Depositional environments of

sandstones as interpreted from electrical

measurements: an introduction, Gulf Coast Assoc.

Geol. Soc. Trans, v. xviii, p.226-241, 1968.

[18] W. L. Fisher, Facies characterization of Gulf Coast

basin delta systems, with some Holocene

analogues. Gulf Coast Association Geol. Soc.

Trans, 1969.

[19] G. D. Klien, “A sedimentary model of determining

paleotidal range” Bull., Geol. Soc. Am., v. 82, p.

2585-2592, 1971.

[20] D. R. Allen, “Identification of sediments-their

depositional environments and degree of

compaction from well logs, in George”, V.

Chilingarian and Karl,H. Wolf, eds., Compaction of

coarse-grained sediments, Developments in

sedimentology, Elsevier, New York, 18 A, p. 349-

402, 1975.

[21] O. Serra and L. Sulpice, “Sedimentological analysis

of sand shale series from well logs”, SPWLA 16th

Ann. Symp. Trans. Paper. P. l-23, 1975.

[22] O. Serra, Sedimentary Environments from Wireline

Logs, Schlumberger, p.21 l, 1985.

[23] O. Serra, 1989, Sedimentary environments from

wireline logs, 2nd ed., Schlumberger, Dallas,

Texas, 1989.

[24] M. H. Bremer, J. Kulenkampff, and J. R. Schopper,

“Lithological and fracture response of common

logs in crystalline rocks”, In Hurst, A., Griffith,

C.M., and Worthington, P.F. (Eds.), Geological

Applications of Wireline Logs II. Geol. Soc. Am.

Spec. Publ., v.65, p. 221-234, 1992.

[25] W. E. Galloway and D. K. Hobday, Terrigenous

Clastic Depositional Systems: Applications of

Fossil Fuel and Ground Water Resources, 2nd ed.,

Springer, Verlag, Berlin Heidelberg, 1996.

[26] G. Uday Bhasker, “Electro lothofacies analysis for

depositional history and stratigraphy of Manuguru

coalfield using geophysical well logs”, J. Ind.

Geophy. Union, v.10 (3), p.241-254, 2006

[27] V. C. Campbell, “Lamina, laminaset, bed and

bedset”, Sedimentology, v.8, p. 7-26, 1967.

[28] J. C. Van Wagoner, H. W. Posamentier, R.M.

Mitcham, P.R. Vail, “An overview of sequence

stratigrpahy and Sea Level Changes: An Integrated

Approach”, Soc. Econo. Paleont. Mineral. Spec.

Pub., v. 42, p. 39-45, 1988.

[29] I. F. Holtrop and I. Keizer, “Some aspects of the

stratigraphy and correlation of the Surma Basin

Wells” East Pakistan, ESCAFF Mineral Resources

Development Series 36, p. 143-154, 1991.

www.cafetinnova.org

Indexed in





ISSN 0974-5904, Volume 07, No. 01

February 2014, P.P.239-250


Structural Analyses of Lesser Himalayan Sequence and Strain

Calculation of the Shergaon Conglomerate of West Kameng District

of Arunachal Pradesh, India

NANDITA MAZUMDAR1, SANTANU BHATTACHARJEE

2, SANDIP NANDY

3 AND K.P.SARMA

1

1Department of Geological Sciences, Gauhati University, Guwahati-781014, Assam, India

2Geological Survey of India, Petrology Division, Hyderabad,

500 068, Andhra Pradesh, India

3Geological Survey of India, CPL, Kolkata-700 016, India

Email: [email protected], [email protected], [email protected], [email protected].

Abstract: The youngest, geodynamically restless, loftiest and most spectacular active mountain belt of continent –

continent collisional tectonics of planet Earth is the Himalaya which creates a structural archive to explore

geological history since Precambrian to Recent. Of the three notable sectors of Himalaya, the western and central

sectors are best studied by scientific communities while eastern sector is still in infancy and needs proper attention,

care, caution and consideration. In the present communication Lesser Himalayan Sequence (LHS) along

Bhalukpong – Tawang – Zimithang geotransect of western Arunachal Himalaya is dealt with from structural

approaches. Two notable conglomerates are mapped around Nagmandir and Shergaon areas of West Kameng

district of Arunachal Pradesh and strain history is worked out. The former separates Bomdila gneiss from Tenga

Formation while the latter separates Dirang Formation from Tenga Formation. The present study is related to

Shergaon conglomerate and the data sets generated from pebbles of conglomerate are populated with mean k =

0.2696 indicating flattening field under simple shear mechanism. Four phases of deformation (D1 to D4) is

established in LHS and their interferences are discussed. Top to the S to SW and rarely SE sense of tectonic

transport is suggested which coincides with the regional kinematics of the stack of thrusted sheets of Arunachal

Himalaya.

Key words: Leseer Himalaya, Shergaon conglomerate, Strain analysis, Western Arunachal Himalaya.

1. Introduction:

Himalaya is an active mountain belt and considered as

storehouse of structural archive or museum of Earth

history. The youngest, loftiest and arguably most

spectacular of all continent-continent collisional belt on

Earth, is the Himalayan Tibetan orogen occurring in the

east-west direction and created by Indo – Asian

collision over the past 70 to 50 Ma[1]

(Yin and Harrison,

2000). About 2500 km long Himalayan mountain belt

(Nagadhiraj of Kalidasa) is one of the classic examples

of most dynamically active and seismically sensitive

orogenic belt of the world forming a curvilinear

disposition of arcuate nature[2]

(Sarma et al., 2011).

Thus, the Great Himalayan orogenic belt creates an icon

of characteristic thrust bound duplex / multiplex

morphology and a stack of important north dipping

tectonic slices bounded by MFT, MBT, MCT, STDC

from south to north and many other locally designated

subsidiary thrusts.

The Lesser Himalayan Sequence of the Bhalukpong –

Tawang geotransect geographically belongs to West

Kameng and Tawang districts of Arunachal Pradesh and

included in Survey of India degree sheet no. 83A. East –

West trending International boundary between China

and India marks the northern boundary of the Tawang

district while the N-S trending International boundary

between Bhutan and India marks the western border

zone. Interstate boundary between Assam and

Arunachal Pradesh sets at the foothills near Bhalukpong

(27001’N:92

038’E).

The Lesser Himalayan Sequence is placed between two

notable thrust systems- Main Boundary Thrust (MBT)

at the lower structural level and Main Central Thrust

(MCT) at a higher structural level from south to north

respectively. The entire Lesser Himalayan Sequence

can be categorically classified into Lesser Himalayan

Sedimentary Sequences (LHSS) and Lesser Himalayan

Crystalline (LHC). The LHSS consists of Tenga

Formation, Dedza / Chillipam Formation, Dirang

Formation and Lumla Formation while the LHC is

represented by Bomdila Gneiss. Quartzite, phyllite,

quartz – sericite schist, quartz-chlorite-sericite schist,

talcose schists with thin bands of amphibolite, para

gneisses and schists, actinolite-hornblende schist, dark





240 Structural Analyses of Lesser Himalayan Sequence and Strain Calculation of the




grey carbonaceous shale, bands of marble/dolomites are

the main lithoassembleges of the LHSS. A distinct

polymictic conglomerate zone comprising deformed

pebbles of quartzite, phyllite, quartz – sericite schist and

quartz clasts of Tenga / Dedza Formation is observed in

the Rupa – Shergaon section which marks an

unconformity with the underlying Tenga Formation and

overlain by Dirang Formation. A similar conglomeratic

zone is also traced near Nagmandir between Tenga

Formation and Bomdila gneiss and it may probably be

the continuation of the Shergaon conglomerate. The

absence of any gneissic pebbles in the conglomerate

indicates that the overlying Bomdila gneiss is younger

than the LHSS. Higher Himalayan sequence (HHS) is

the northernmost exposed part of the Indian plate and is

separated from LHS by MCT. LHS is characterized by

greenschist to lower to middle amphibolite facies

metamorphism while HHS portrays metamorphic

signature upto amphibolite facies. LHS is separated

from sub Himalayan Siwalik molasses type sediments

by MBT. Thus MFT, MBT, MCT constitute imbricate

thrust system on the southern part of the Himalayan

orogen under contractional tectonism whereas STDS

(not observed in the present area) registered extensional

tectonic mechanism to the north.

Thus, all the tectonic slices are considered to be the

counterpart of the north facing Himalayan passive

continental margin commonly named as Tethyan

Himalaya which develop from Middle Proterozoic to

Cretaceous time[3,4]

(Colchen et al., 1982; Brookfield,

1993).

Structural analysis of the crystalline rock between

Dirang and Tawang sector (HHS) have been worked out

by[5]

Srivastava et al., (2011). Similarly[6,7,8]

Goswami

et al. (2009), Saha (2013), Bhattacharjee and Nandy

(2008) also have discussed the structural history of the

rocks of West Kameng and Tawang districts, but LHS is

least understood and therefore, an attempt is made in

this communication to discuss the deformational history

of the Lesser Himalayan Sequences (LHS) along with

the strain history of the Shergaon conglomerates.

2. Regional Geology:

The Bhalukpong – Tawang – Zimithang sector of

Western Arunachal Himalaya witnessed different

lithocomponents from Proterozoic to Pleistocene period

and a series of tectonic contacts and thrusts from south

to north i.e. from lower to higher structural levels are

delineated. Pleistocene/ alluvium zone represents

southern end of the lithounits thrusted over by Siwalik,

Gondwana, Lesser Himalaya and finally Higher

Himalayan belt marks the northern end of the

geotransect. Most of the lithounits are highly deformed,

intensively sheared and metamorphosed and registered

the imprints of deformational phases and associated

metamorphic signatures.

An anticlinal fold structure of isoclinal geometry is

observed near Bhalukpong at the lower structural level

which marks the Main Frontal Thrust (MFT) separating

Siwalik from alluvium[9]

(Yin et al., 2006). The footwall

side of the conventional MBT (traceable at 27005’20”N:

92035’18”E) is occupied by Eocene marine and volcanic

strata bound sequences[10]

(Kumar, 1997). The MBT

constitutes a zone, forming lower and upper MBT1 and

MBT2 respectively and the latter separates Permian

sequences from overlying Proterozoic Bomdila Group

comprises of Dedza Formation, Tenga Formation and

Dirang Formation and they are intruded by large scale

Bomdila granite gneiss. Carbonaceous phyllite, phyllite

and dolomitic limestone are the main components of the

Dedza Formation; Tenga Formation is constituted by

quartzite, mafic meta volcanics and phyllites. The

Dirang Formation consists of garnet-kyanite-staurolite

bearing metapelite, quartzite, phyllite, metavolcanics

including amphibolites, quartz – actinolite schist and

they form footwall of the MCT zone. Low grade

metamorphism of the basal Lesser Himalayan Sequence

is structurally overlain by megacrystic granitic gneiss

(Bomdila gneiss) and an undoubted tectonic contact is

noted by earlier researchers. It has a linkage with

Cenozoic thrusting upliftment mechanism and

represents a tectonic counterpart of Palaeo to

Mesoproterozoic basement rocks of Indian

subcontinent.

The Dirang Formation is structurally overlain by garnet-

kyanite-sillimanite bearing metapelitic rocks,

leucogranite, garnetiferous amphibolite, calc-silicate

rocks, sillimanite bearing quartzofeldspathic gneiss and

migmatites together forming “SeLa Group” on the

hanging wall side of the MCT zone. Around 4 km from

Dirang on way to Tawang, the MCT is observed

(27022’42”N: 92

013’54”E). Presence of hot spring along

the interface between SeLa Group and Dirang

Formation is another signature indicating the presence

of a thrust namely MCT (= Dirang Thrust). [8,6]

Bhattacharjee & Nandy (2008), Goswami et al. (2009)

have the opinion that the MCT marks as a 5-7 km

ductile zone rather than a single line similar to that of

MCT zone of Bhagirathi valley[11]

(Metcalfe, 1993).

Beyond Tawang towards Zimithang, a huge close

outcrop named as Lumla Formation is observed and has

been referred to as tectonic window[12,9,8]

(Tripathy et

al., 1979; Yin et al., 2006; Bhattacharjee & Nandy,

2008) and equated with the rocks of Dirang Formation.

The southern tectonic contact of Lumla Formation with

SeLa Group is marked at 27033’14”N: 91

045’29”E

while the northern contact with Zimithang granite is

marked at 27037’48”N: 91

043’16”E, 35.5 km from

Lumla towards Zimithang. Zimithang granite is a huge

241 NANDITA MAZUMDAR, SANTANU BHATTACHARJEE, SANDIP NANDY AND K.P.SARMA



batholithic body of deformed to partly undeformed

coarse grained leucocratic to mesocratic granite,

International boundary between India and Tibet is

passing through this granite massif at a few kilometers

north of Zimithang and hence tracing of its northern

limit is beyond our scope. Formation of Shonga-tser

Lake (popularly known as Madhuri lake) at

27043’40”N: 91

049’42”E is an imprint of

Neoproterozoic activity near International Boundary

between India and Tibat. Its contact with the Sela Group

is traceable towards east and SE of Zimithang – Tak –

Tsang Gompa road. The Zimithang granite is thrusted

over the Lumla Formation and this thrust is considered

as upper limit of the Lumla Thrust or may be referred to

as “Zimithang Thurst” (ZT) [2]

(Sarma et al., 2011)

equivalent to Kaktang thrust of Bhutan[13]

(Gansser,

1983).

A lithotectonic map is presented in figure 1.

Fig1: Geological map of the study area

3. Structural History Of Lesser Himalayan

Sequence:

It is generally accepted that the Himalayan orogenic belt

displays characteristic thrust duplex morphology and

accommodate a number of south vergent thrusts. The

involvement of basement rocks of the Indian plate in the

Himalayan orogen display a great role in the structural

evolution and tectonic framework of the orogen by

moving vertically up because of buoyancy and pushed

southward over the younger rock sequences[14]

(Bhattacharya, 2008). The Himalayan metamorphic belt

(HMB) along Bhalukpong- Tawang- Zimithang

geotransect bears the identities of deformational fabrics

belonging to Pre-Himalayan, Syn-Himalayan and Post-

Himalayan episodes from Proterozoic to lower

Pleistocene periods. HMB has undergone at least four

phases of deformation D1 to D4[15]

(Jain et al., 2002)

and they argued that Himalayan granitoids (1800-2000

Ma) contains relict Palaeoproterozoic structures.

The study area is a part of the thrust bound geounits i.e.

the different tectonostratigraphic zones are separated by

a number of major thrusts namely MFT, MBT and MCT

in addition to a few minor thrusts. South Tibetan

Detachment System (STDS) separates the Tethyan

sedimentary zone of the south Tibet from Higher

Himalyan Sequence (HHS) (not observed in Indian

subcontinent) and the latter is thrust over Lesser

Himalyan Sequence (LHS) by a zone of high ductile

shear strain, traditionally designated as MCT. Thrust

morphology in the hanging wall side of MCT registered

top to the south tectonic transport under N-S tectonic

regime. LHS is thrusted over Siwalik sequences with a





top-to-south vergence geometry and acts as the

hinterland of the foreland Siwalik sedimentation.

Deformational history of the Lesser Himalayan

Sequences of West Kameng and Tawang districts of

Western Arunachal Pradesh has been discussed by a

number of authors[16,10,9,8,6,5,17,7]

(Bhusan et al., 1991;

Kumar, 1997; Yin et al., 2006; Bhattacharjee and

Nandy, 2008; Goswami et al., 2009; Srivastava et al.,

2011; Sarma et al., in press; Saha, 2013). [8]

Bhattacherjee and Nandy (2008) have suggested two

phases of deformation in the Lesser Himalayan

Sedimentary sequences and one phase of deformation in

Lesser Himalayan Crystalline (Bomdila gneiss). [6]

Goswami et al. (2009) also have suggested three

phases of deformation (D1 to D3) and two groups of

planar structures (S1 and S2). [5]

Srivastava et al. (2011)

have delineated four phases of folding F1, F2, F3, and F4

where F1 and F2 are coaxial.

The present study deals with the structural analyses on

mesoscopic scale of the Lesser Himalayan Sequence of

western Arunachal Himalaya and strain analysis of

Shergaon conglomerate. The lithounits of western

Arunachal Himalaya exhibit structural trend parallel to

the general trend of the major thrusts (i.e. NE-SW) with

a steep to moderate dip towards NW. Both brittle and

ductile deformational effects are seen in these lithounits.

The LHSS (consisting of Dirang and Lumla

Formations) composed of phyllites, carb-phyllites,

metapelites, quartz-mica schist, micaceous quartzite,

quartzite, limestone, phyllonite and mylonites with

metavolcanics like actinolite-chlorite schist and

amphibolites. Generalised strike and trend of the Dirang

metasedimentaries are NE-SW with an average dip 400-

600 towards NW. The LHSS display structural identities

of four different phases of deformation resulting planar,

linear and fold fabrics and typical interference patterns

are imprinted on them. On the regional scale, pervasive

planar fabric is designated as CS2 shear foliation, a

planar fabric developed during Himalayan orogeny (=

S2 of [6]

Goswami et al., 2009). Pre Himalayan

signatures are still preserved in metasedimentaries and

they act as relict F1 fold associated with axial planar

foliation S1 (Fig. 2a). S1 strikes NE – SW showing

generalized NW dip and moderate angle. F1 is close,

appressed, isoclinals type and the contemporaneous

foliation transects S0 at high angle at the hinge zone of

F1 (Figs. 2a, 2b). Such fabrics are readjusted and

restructured during Himalayan orogeny resulting

pervasive shear foliation irrespective of lithounits and

further affected by successive deformational phases and

their interferences (Figs. 2c, 2e). In amphibolite, rarely

S1 is observed in the hinge zone of minor F2 folds and

intersect CS2 at high angle. Crenulations and folds on

minor scale are observed with a generalised axial

orientation NW-SE (Fig. 2d). F2 and F1 folds maintain

coaxiality in some cases. F2 plunges 400 to 60

0 towards

W or SW. Southeastern limb of F2 is mostly short and

steep while northwestern limb is gentle and long.

Overturning character of F2 is marked in many places

showing top to S or SE vergence (Fig. 2e). The

superposition of third phase deformation is documented

by metasedimentaries and metavolcanics of LHSS (Fig.

2f). The generalised axial orientation of F3 fold is NW-

SE showing plunge towards NW at moderate ≈ 450

angle (Fig. 2f). The behaviour of F3 is moderately

closed, open to warp type and the fold pattern and

geometry indicates with top-to-SW vergence as against

the S – SE vergence of F2. Stretching lineation is

correlatable to D3 deformation indicating NW to NNW

slip direction. Mild curvature of the axial orientation of

F3 trending roughly N – S, minor kink fold in

incompetent rock units, small scale faults and fractures

observed in multiple folds are classified as F4 (Fig. 2d).

Fig2a: Tight appressed F1 fold in Dirang Formation of

LHSS with thickened hinge and relatively thin limbs

plunging NE, axial plane is near horizontal, location:

south of Dirang.

Fig2b: Tight isoclinal fold marked by quartzite layer

from Dirang Formation near contact zone between

Bomdila gneiss and Dirang Formation. The axial plane

is near vertical and axis is near horizontal.




Fig2c: Alternate layers of quartzite and metapelite of

Dirang Formation showing interference between F1 and

F2 asymmetric folds, plunging towards NE. photograph

facing NE.

Fig2d: Highly deformed phyllite of LHSS, south of

Nagmandir area showing kinking (F3) with near

horizontal axial plane trending N-S. Minor crenulations

show low angle plunge due NW.

Fig2e: Hook shaped interference pattern between F1 and

F2 associated with CS2 in Dirang Formation. Location:

contact zone of Bomdila gneiss and Dirang Formation.

Fig2f: Open, upright F3 fold in Lumla Formation with

dextral motion. Location: near Lumla.

Fig2g: Nagmandir conglomerate separating LHSS from

LHC.

Fig2h: Shergaon conglomerate with highly stretched

pebbles parallel to subparallel to CS2 (E-W direction)





Fig2i: Crenulated Bomdila gneiss showing subsidiary

top left bottom right shearing (S3) transecting CS2.

Fig2j: Close to tight F2 is marked by quartzite and thin

metapelite. CS2 is axial planar

Fig2k: Alternate M and Q domain in metapelite, folded

by F3 of open upright type.

Fig2l: Syn D2 garnet with sygmoidal Si fabric of dextral

sense.

Bomdila Gneiss (BG) referred to as orthogneiss is

tectonically emplaced over Bomdila Group. The LHC of

the western Arunachal Himalaya encloses a number of

felsic and mafic enclaves registering the testimony of

earliest planar fabric (S0=S1) and relict intrafolial,

rootless folds of first phase of deformation. Such folds

are probably a remnant of Indian continental crust

survived during the process of intensive shearing and

restructuring of syn-Himalayan orogeny. The syn-

Himalayan orogeny was so intense that most of the

earlier fabrics of Proterozoic basement rocks of the

Indian plate were destroyed, transposed, restructured

and developed most pervasive ductile shear foliation

(CS2), stretching lineation (L2), reclined fold (F2) and

sheeth folds (F2). Therefore, CS2 foliation acts like tape

recorder where subsequent fabrics of D3 and D4 under

continued compressional regime were recorded.

Mylonitic foliation is marked by preferred orientation of

elongate or elliptical phenocrysts of feldspar, quartz and

rarely garnet (Figs. 2j, 2k). They also define stretching

lineation and direction varies from NW to NNE.

Variation from augen gneiss to ultramylonite through

mylonitic gneiss is observed along the outer western

marginal zone of the Bomdila gneiss and they show

high degree of dip due west (Fig. 2i, 2j). [9]

Yin et al.

(2006) have suggested that the mylonitic foliation in

Bomdila gneiss is a fabric developed during Indo –

Asian collision and not an inherited pre Cenozoic

structure. CS2 fabric is folded by open, asymmetric to

overturned folds (F3) showing moderate to high angle

plunge (400-70

0) towards NW to W in the western

boundary of BG, in the northern boundary moderate

plunge (~ 450) due N to NE and in the southern

boundary reversal of plunge either S or SE are also

observed. Such reversibility, either may be due to the

effect of later deformation (D4) or may be the imprints

on earlier thrusting configuration. Stretching and




mineral lineation (L2) plunges at low to moderate angle

due NE and /or SW.

Discrete subsidiary shear bands of non-pervasive nature

which wraps round augens of varied dimension marks

the registration of D3 deformation in BG (Fig. 2k). The

orientation of the slip planes of fragmented augens with

dextral motion, minor faulting in the matrix and also in

the mafic enclaves follow the structural weak locals of

D3 deformational episodes (Fig. 2l). Interference

between F2 and F3 marks the interference pattern 1

(dome and basin structure) in associated LHSS but they

are infrequently traceable in Bomdila gneiss rather

interference patterns- 2 and 3 - are seen where intensity

of strain is maximum and the pervasive shear bands

mark the flowage of disharmonic nature.

Emplacement of the vein rocks along N-S orientation

probably follows the structural locales of D4 phase.

Kink band, minor faulting, N-S trending quartz,

tourmaline, feldspar veins and brittle fractures are seen.

Emplacement of dolerite and basaltic dykes is although

hardly correlatable with a definite deformational phase

but can be categorically placed under post Himalayan

orogenic cycle, free from metamorphism and follow D2,

D3 and D4 structural locals in the NE-SW, NW-SE and

N-S directions. The latter two directions truncate

regional orientation of the different lithocomponents of

the Himalayan Metamorphic Belt in the context of

subducted Indian plate configuration.

3.1. Microscopic Structures:

The rocks of the LHSS have undergone repeated

deformation cum metamorphic transformation during

Himalayan orogeny. Intensive structural readjustments

during Himalayan orogenic movement have either

erased away most of the Pre Himalayan Indian plate

related microscopic /mesoscopic structural evidences

except some small scale isoclinal to tight

appressed folds and rarely preserved planar fabrics in

the hinge zone of F2 folds of coaxial nature with F1

where dominant shear foliation (CS2) maintain cross cut

relationship. Such fabrics are rarely preserved in the

mafic enclaves within Bomdila gneiss. Synhimalayan

ductile phase results CS2 all throughout the rock units

(LHSS and LHC) and is axial planar to F2 showing

structural trend roughly NE-SW with moderate plunge

either NE or SW direction (Fig. 2j). In metapelite both

M and Q-domains are observed and they folded by F3

and wrap round garnet porphyroblast showing both σ

and δ type of rotation. Garnet bears the identities of

straight trails of inclusion, sygmoidal rotation and also

intertectonic stage bearing Se fabric as Si within garnet

(Fig. 2l). In garnetiferous phyllite of Dirang / Lumla

Formation continuous cleavage (CS2) is marked by

flattened quartz, parallel alignment of biotite and

muscovite and sometimes elongate skeletal garnet or

garnet aggregates (Fig. 2k). Three generations of micas

(M1 to M3 are identified: (a) small flakes occurring as

inclusion in garnet or feldspar porphyroblasts (M1), (b)

as big flakes of mus2 and biot2 defining pervasive

foliation (CS2) which often wrap round different

porphyroblast or sometimes truncates (M2). They define

folding of later generation (F3 and F4) and (c) as broad

and short flakes superposed on CS2 at different angle

mostly along strain zones of F3 and F4 folding (M3).

Thus M1 is interpreted as D1, M2 as Syn D2 and M3 as

syn to post D3 stages of folding.

Fig2m: CS2 is folded by F3 fold; S3 is axial planar to F3.

Fig2n: Interference of F2 and F3 in alternate metapelite

and quartzite

Actinolite hornblende also marks similar behaviour. In

LHC, the protolithic feldspar phenocrysts suffer tectonic

attenuation and form augen defining CS2 with mostly

right lateral sense of rotation. Such asymmetric

vergence marked by rotational movement of the strain

markers and associated folding is a clear indicative of

non – coaxial deformation under simple shear

movement picture. CS2 is highly crenulated showing

extension crenulation cleavage, zonal crenulation

cleavage and fracture planes (without growth of new





minerals) (Figs. 2m,n). Interference of F2 and F3 is well

defined in metapelites of the Dirang and Lumla

Formation of LHSS. Intragranular kinking and

microfaulting of extensional habit in feldspar augens are

also seen. Shearing and grain granulation leads to

anastomosing foliation and mortar texture. Metapelites

and metabasites from foreland part of the MCT zone

show high degree of shearing and intensive quartz

veining from both Dirang and Lumla areas. Similar

observations are also observed along the Lumal –

Bhutan road.

4. Strain Analysis:

Strain in rocks can be calculated with the help of strain

markers such as ooids, spherulites, radiolarian shells,

foraminifera, pebbles of conglomerates, brecciated

mass, augens, ribbon quartz, amygdoles etc. In this

communication, deformed pebbles of conglomerate

observable at mesoscopic scale were considered as

strain marker to quantify finite strain (Fig. 2h). It is

difficult to ascertain whether initially the pebbles of

conglomerate were spherical or elliptical but the present

disposition of pebbles act as kinematic indicators. For

comperative study and correlation purpose, strained

quartz from associated rock components are also

considered side by side. They exhibit significant

stretching and rotation when they undergo deformation.

The conglomerates near Shergaon (hereafter will be

referred to as Shergaon conglomerate) are highly

deformed, stretched, fragmented, rarely faulted and

rotated as against the Nagmandir conglomerate which is

less deformed (Figs. 2g, 2h).

The generalized strike of the conglomeratic horizon is

NE-SW and the long axes of the pebbles are generally

parallel to the SC foliation cum interfaces of the

underlying lithosetting (Fig. 2h). In the field, pebbles

are measured on the XZ plane as well as YZ plane and

their long and short axes are calculated. The average

size varies from 0.77 – 12 cm in length (X) and 0.36 – 4

cm in breadth (Z). In one road section, YZ section of the

conglomerate horizon is exposed wherefrom

photographs and a few measurements are taken. [18]

Ramsay (1967) described Rf/ technique for measuring

strain from any deformed strain markers and

subsequently it was modified by [19]

Dunnet (1969) and [20]

Lisle (1977). It is not possible to ascertain the

original size of the strain markers before deformation

even if the shape parameter is known. Hence, some

sorts of assumptions are made to proceed for their

calculations. Similar is the case of initial orientation of

such markers. As manual calculations with some

amount of assumptions is relatively time consuming,

therefore, computer based software are used in the

present presentation. Initially, it was thought of that the

strain estimation could be made by Fry method from

isotropic anti clustered distributions of strain markers

that was deformed homogeneously[21,22]

(Fry, 1979;

Hanna and Fry 1979). But often it is observed that the

strain markers are affected by heterogeneous

deformation and the original pre deformational centres

of the markers are difficult to define. Therefore,

calculated centroids will underestimate finite strain in

Fry plots and as such more is the heterogeneity more is

the error. Out of different methods available for strain

analysis, only four methods are adopted for the present

study namely (1) Flinn plots [23]

(Flinn 1962), (2)

Ramsay and Wood plot [18,24]

(Ramsay 1967; Ramsay

and Wood 1973), (3) Rf / plots[18,19,25]

(Ramsay 1967;

Dunnet 1969; Dunnet and Siddan 1971), (4) Fry method [26,22]

(Fry 1978, 1979; Hanna and Fry 1979). These

plots are prepared using the software ‘Sixstrain’

developed by [27]

P.P.Roday (2003).

Section wise pebbles are drawn on transparent overlays

and field photographs were taken in the field. The

conglomerate is so friable that it is hardly possible to cut

the sample in required orientation. Even it was not

possible to collect oriented samples with respect to

lithological layering or dominant CS2 foliation of Syn

Himalayan deformation. However, photomicrographs

are made use of in preparing strain diagrams. The

lengths of the long (X), intermediate (Y) and short (Z)

axes of the deformed pebbles are measured with the

help of transparent overlays and enlarged photographs

with scale. Axial ratios (Rf) of XZ and YZ sections and

orientation of major axes with respect to the reference

line () is also calculated. Recently, excel supported

spread sheet based approach to Rf/ strain analysis was

formulated by [28]

Chew (2003) which is more easier

method in calculating symmetry of Rf/ plot and initial

orientation of the strain markers.




Fig3a: Flinn plot

Fig3b: Ramsay and Wood plot

Fig3c: Rf/ plot of XZ section

Fig3d: Rf/ plot of YZ section





Fig3e: Fry plot of Shergaon conglomerate

Flinn diagram prepared from Shergaon conglomerate

reveal that has all the plots lie in the flattening field

(oblate type) with characteristic mean value k = 0.2696,

which is less than 1 indicating simple shear mechanism

(Fig. 3a). [24]

Ramsay and Wood (1973) plot also

indicate the flattening field (k = 0.3684) (Fig. 3b). From

graphical plots of Rf vs. finite strain (Rs) values were

determined by visual best fit into the standard curves of [19]

Dunnet (1969). The plots of the data in Rf / plots

indicate that the angle is relatively less in XZ section

than that of YZ section. The population of Rf values is

not highly scattered but they have a rather narrow

values (Figs. 3c, 3d). It is seen than the vector mean of

the pebble long axes on the YZ section is not parallel to

the CS2 plane. Fry plots are prepared from photographs

as per standard methods show an elliptical vacant area

of no concentration around the central part. The average

ratio of long and short axes is 2.47 (Fig. 3e).

In the outcrop scale strain appears to be homogeneous

but on the regional scale, heterogeneity prevails on the

entire Lesser Himalayan Sedimentary Sequence.

5. Discussion:

Western Arunachal Himalayan Block (WAHB) bears a

true Himalayan signature which is a lateral strain

extension from western Himalayan through Nepal,

Sikkim and Bhutan Himalaya upto Bame fault, whereas

Mishmi Himalaya is a separate geounit tectonically

thrusted from Mogok Belt of Burma[29]

(Nandy, 2001),

juxtaposed like a tectonic roof over two pillars like

WAHB and Indo Myanmar Mobile Belt (IMMB) [2]

(Sarma et al., 2011).

The two major components of LHS are LHSS and LHC;

the latter is tectonically emplaced over less or

unmetamorphosed rocks of LHSS. The LHC in

Bhalukpong – Tawang sector is represented by

Palaeoproterozoic Bomdila gneiss. Whole rock Rb-Sr

isochron ages of Bomdila gneiss is marked out as 1914

± 23 Ma [30]

(Dikshitala et al., 1995); 1676 ± 122 Ma [31]

(Bhalla and Bishui, 1989); 1743 ± 4 Ma from zircon

study by [32]

Yin et al. (2010) and they all are related to

Meso to Palaeoproterozoic age.

[5] Srivastava et al. (2011) have suggested that the Indian

plate is moving northward and collided with Eurasian

plate pushing backward all the rock masses southward

in the form of tectonic slices either as imbricate thrust,

schuppen zone, duplexes or multiplex. Their early,

middle and late phases of deformations are correlatable

with Pre Himalayan, Syn Himalayan and Post

Himalayan phases of [15]

Jain et al. (2002). On the other

hand, inferred SeLa and Tawang thrusts as suggested by [5]

Srivastava et al. (2011) are correlatable with MCT1,

MCT2 and MCT3 of [33]

Valdiya (1980) from Western

Himalaya.

[32] Yin et al. (2010) have mapped the Bhalukpong –

Zimithang geotransect on regional scale and shown a

good number of sections including large scale SeLa

synclinorium in the Higher Himalayan sequence. The

large scale shear sense top to the SE to S or SW worked

out both from minor and major structures are suggested

to be due to superposition of Precambrian and Tertiary

Deformations.

The present study is confined only to Lesser Himalayan

sequence unravelling the presence of conglomerates

near Dedza and named as Nagmandir conglomerate

separating LHC (Bomdila gneiss) from Rupa Group (=

Tenga Formation = Dedza Formation which is

equivalent to Bauxa Formation) showing numerous

pebbles of limestone, phyllite, quartzite and quartz.

Similarly, another conglomerate is traceable at 5 km

ahead of Shergaon and named as Shergaon

conglomerate with significant stretched pebbles,

cobbles, quartz sericite schist, limestone and quartz

pebbles. Occurrences of these two conglomerates

indicate that Tenga Formation is older than Dirang

Formation as well as Bomdila gneiss.

6. Conclusions:

The present study is a synchronization of some of the

early workers observations. The following observations

are suggested. Microstructural identities indicate that

intensive mylonitic fabric of syn Himalayan orogeny

were deformed by crystal plastic and strain softening

mechanism under low to moderate pressure –

temperature conditions within lower to middle part of

the amphibolites facies. Computed strain related




datasets are populated in the flattening field hints

moderate to high shear strain.

Based on vergence pattern of small scale folds of

different generations and scales, the sense of asymmetry

is worked out and it is observed that Pre Himalayan and

Syn Himalyan structural fabrics are showing top to the

SE to SW through S sense of shear. On the regional

scale, slip vector may be considered as top to the south

sense of tectonic transport. This kinematic direction

coincides with the regional kinematic directions of

MFT, MBT and MCT [32]

(Yin et al., 2010).

Thus, it is concluded that the rocks of the western

Arunachal Himalaya in the Bhalukpong – Tawang

sector which represents part of the Indian continental

crust display compressional - collisional tectonism

between Indian and Eurasian plates in a near horizontal

tectonic setup followed by stack of intensive thrusting

where rotational axes coincides with the x-direction of

maximum extension.


The authors are thankful to the Department of Science

and Technology (DST), Government of India for

providing financial assistance in the form of the project

(ESS/16/242/2005/Kameng(06)). The authors would

also like to acknowledge the Department of Geological

Sciences, Gauhati University, Guwahati and Geological

Survey of India for providing facility to carry out the

work.

8. Reference:

[1] Yin, A. and Harrison, T.M. (2000). Geologic

evolution of the Himalayan Tibet orogen. Annual

Review of Earth Planet Science 28 211 – 280.

[2] Sarma, K.P., Bhattacherjee, S., Nandy, S., Konwar,

P. and Mazumdar, Nandita (2011). Thrust bound

lithounits of Western and Eastern sectors of

Arunachal Himalaya, India: An integrated approach

of correlation. Memoir Geological Society of India

77 33-41.

[3] Colchen, M., Bassoullet, J.P., Mascle, G.l., (1982).

La Palaeogeographie des orogenes, l’example de

l’Himalaya. Memoir Geol. l’Univ. Dijon. 7, 453 –

71.

[4] Brookfield, M.E. (1993). The HimalayanPassive

margin from Precambrian to Cretaceous.

Sedimentary Geology 84 1 – 35.

[5] Srivastava, H.B., Srivastava, V., Srivastava, R.K.

and Singh, C.K. (2011). Structural analyses of the

crystalline rocks between Dirang and Tawang,

West Kameng District, Arunachal Himalaya.

Journal of Geological Society of India 78 45 – 56.

[6] Goswami, S., Bhowmik, S.K. and Dasgupta, S.

(2009). Petrology of a non-classical Barrovian

inverted metamorphic sequence from the western

Arunachal Himalaya, India. Journal of Asian Earth

Science 36 390-406.

[7] Saha, D. (2013). Lesser Himalayan sequences in

Eastern Himalaya and their deformation:

Implications for Palaeoproterozoic tectonic activity

along the northern margin of India. Geoscience

Frontiers, doi: 10. 1016/j.gsf.2013.01.004.

[8] Bhattacharjee, S. and Nandy, S. (2008). Geology of

the Western Arunachal Himalaya in parts of

Tawang and West Kameng districts, Arunachal

Pradesh. Journal of Geological Society of India 72

199-207.

[9] Yin, A., Dubey, C.S., Kelty, T.K., Gehrels, G.E.,

Chow, C.Y., Grove, M. and Lovera, O. (2006).

Structural evolution of the Arunachal Himalaya and

implications for asymmetric development of the

Himalayan orogen. Current Science 90 195-206.

[10] Kumar, G. (1997). Geology of Arunachal Pradesh.

Journal of Geological Society of India 217.

[11] Metcalfe, R.P. (1993). Pressure,temperature and

time constraints on metamorphism across the Main

Central Thrust zone and High Himalayan Slab in

the Garhwal Himalaya. Himalayan Tectonics,

Geological Society of London Special Publication

74.

[12] Tripathi, C., Jain, L.S. and Basu Roy, S. (1979). A

note on the find sulphide mineralisation in Lumla

area, Kameng district, Arunachal Pradesh. Indian

Minerals 33(2) 55.

[13] Gansser, A. (1983). Geology of the Bhutan

Himalaya, Basle. Denkenschrift der

Schweizerischen Naturforschenden Gesellschaft.

Band. 96. Basel, Birkhauser, 181.

[14] Bhattacharya, A.R. (2008). Basement rocks of the

Kumaun – Garhwal Himalaya: Implications for

Himalayan Tectonics. e-Journal Earth Science India

1(1) 1 – 10.

[15] Jain, A.K., Singh, S. and Manchavasagam, R.M.

(2002). Himalayan collision Tectonics. Gondawna

Research Group Memoir 7 114.

[16] Bhusan, S.K., Passayat, R.N. and Agarwal, R.K.

(1991). Preliminary investigation for base metal

mineralisation near Shergaon, West Kameng

district, Arunachal Pradesh. Records Geological

Survey of India 124(4) 115-127.

[17] Sarma, K.P., Bhattacherjee, S., Nandy, S., Konwar,

P. and Mazumdar, Nandita (2012). Structure,

Stratigraphy and Magnetic Susceptibility of

Bomdila Gneiss, Western Arunachal Himalaya,

India. Journal of Geological Society Of India

(accepted).

[18] Ramsay, J.G. (1967). Folding and fracturing of

rocks. New York: McGrow Hills 568.

[19] Dunnet, D. (1969). A technique of finite strain

analysis using elliptical particles. Tectonophysics

7(2) 117–136.





[20] Lisle, R.J. (1977). Estimation of the tectonic strain

ratio from the mean Shape of deformed elliptical

markers. Geologie en Mijnbouw 56 140 – 144.

[21] Fry, N. (1979). Random point distributions and

strain measurement in rocks Tectonophysics 60

89–105.

[22] Hanna, S. and Fry, N. (1979). A comparison of

methods of strain determination in rocks from SW

Dyfed (Penbrokeshire) and adjacent area

Tectonophysics 5 315–319.

[23] Flinn, D. (1962). On folding during three-

dimensional progressive deformation. Quarterly

Journal of the Geological Society of London 118

385–433.

[24] Ramsay, J.G. and Wood, D.S. (1973). The

geometric effects of volume change during

deformation processes. Tectonophysics 13 163–

271.

[25] Dunnet, D. and Siddan, A.W.B. (1971). Non

random sedimentary fabrics and their modification

by strain. Tectonophysics 12 307–325.

[26] Fry, N. (1978). Construction and computation of 3-

D progressive deformation. Journal of Geological

Society of London 135 291–305.

[27] Roday, P.P. (2003). Windows 32-Bit Platform

Software for plots to display the finite strain data.

Journal of Geological Society of India 62 36–42.

[28] Chew, D. (2003). An Excel spreadsheet for finite

strain analysis using the Rf – phi technique.

Computers & Geosciences 29 (6) 795 – 799.

[29] Nandy, D.R. (2001). Geodynamics of northeastern

India and the adjoining region. Abc publication,

Kolkata 209.

[30] Dikshitala, G.R., Pandey, B.K., Krishna, V. and

Dhana, R. (1995). Rb – Sr systematic of granitoids

in the Central Gneissic Complex, Arunachal

Himalaya: Implication on tectonics, stratigraphic

and sources.Journal of Geological Society of India

45, 51–61.

[31] Bhalla, J.K. and Bishui, P.K. (1989).

Geochronology and Geochemistry of granite

emplacement and metamorphism of north eastern

Himalaya. Records Geological Survey of India 122

8 – 20.

[32] Yin, A., Dubey, C.S., Kelty, T.K., Webb, A.A.G.,

Harrison, T.M., Chou, C.Y. and Célérier, Julien

(2010). Geologic correlation of the Himalayan

orogen and Indian craton: Part 2. Structural

geology, geochronology, and tectonic evolution of

the Eastern Himalaya. Bulletin Geological Society

of America 122(3/4) 360 – 395.

[33] Valdiya, K.S., (1980). Geology of Kumaun Lesser

Himalaya, Wadia Institute of Himalayan geology,

Dehradun, India, 291p.

www.cafetinnova.org

Indexed in





ISSN 0974-5904, Volume 07, No. 01

February 2014, P.P.251-259


A Geo-Technical assessment of Slope stability condition at Lovedale

Club slide, Lovedale, The Nilgiris, Tamil Nadu, India

E. SARANATHAN, SUGANYA KANAGASABAI, M. KANNAN AND G. K. VENKATRAMAN School of Civil Engineering, SASTRA University, Thanjavur, INDIA


Abstract: Natural disaster like landslides mostly seen in a fragile mountain hill slopes like Himalayas in North

India, Western ghats in Kerala and Karnataka, The Nilgiris, Annamalai hills, Megamalai in Tamil Nadu, etc.

Anthropogenic activities like improper planning, networking, deforestation and agricultural activities are one of the

important factors in hilly regions and aggravated the slope instability. Normally, wherever, slides are occurs the

slope is converted to gentle slope and it is stable in condition. In this condition, due to external factors like rainfall,

manmade activities are further stressed and causing recurrence of slope instability in an already slide slopes. In this

regard, to assess the stability condition of slide area, which seen in recent past near human settlement are taken to

detailed study.

The present study area, in Lovedale, The Nilgiris one of the connecting roads from NH 67 to Lovedale village have

been obtained considering stress condition due to heavy urban development’s (mainly resorts) in this ghat section.

In Ooty municipality, 25 slides are occurred in 2009 rainfall, out of 25 slides five slides are present in this ghat

section and one of the slide points near Lovedale Club was selected in detailed study. Due to this slide, the club

building is damaged and middle slope NMR railway was also blocked by debris. The field investigation noticed that

3 tensional cracks present in the upper slope near club building. It is clearly documented through photographs. The

slope was divided into three zones as upper, middle and lower slope and slope stability analysis was carried out. To

fulfilling the study, nine surface soil samples and three core samples were collected in upper, middle and lower

slopes and found out ‘c’ and ‘’ values using Direct Shear Test. The factor of safety was calculated by using Limit

Equilibrium method. As per the analysis, factor of safety was calculated in three static conditions as dry, partial

saturated and complete saturated condition. The results indicated that upper slopes FOS is 0.98, 0.84, 0.70 as dry,

partial and complete saturated conditions, middle slope is 1.37, 1.09, 0.80 and lower slopes is 1.32, 1.05, 0.77 for

same conditions respectively. The results are verified with CFC method. The slide area is very critical in condition

whenever heavy precipitation is present and the slope may be failed. The results are informed to local administrative

agencies and recommended to prevent the slope using any one of the slope production measures.

1. Introduction:

Landslides in India are common phenomena in any hilly

regions. However, in other hill stations and in some

plateau regions, it appears now and then. Landslides

cause extensive damages to roads, bridges, human

dwellings, agricultural lands, forest, etc., resulting in

loss of property as well as human life. It frequently

occurs in hilly regions like Himalayas, Western and

Eastern Ghats. In Tamil Nadu, often landsides are seen

in Ootacamund, Kodaikanal, Yercaud and occasionally,

in the other areas (Anon 2006, 2007 and 2009). The

Ootacamund is located at the confluence of the Eastern

and Western Ghats. The Ooty town is present in

intermountain valley of Nilgiris. The town is located at

2240m elevation above MSL. It is also called Queen of

hill stations. After independence, the Urban have been

witnessing faster growth, due to a number of

developmental activities proposed by the Government.

The growth of settlement has been remarkable in the hill

stations without any plan. This result in environmental

degradation, mainly land use changes in the hilly areas

induced landslides. Landslides are trembling in the

whole Nilgiris Mountain.

The Ooty town covers an area of about 30 sq. km. It

may be noted that the built-up land constituting about

60% of the total area and remaining area coming under

agricultural practices. Ootacamund fascinates a vast

number of people during the summer months. During

this period, the town attracts about two lakh person per

day. In off-season, an average tourist flow is of about

15,000 to 20,000 persons per day. The foreign tourists

account to a total of about 30 to 40 thousand persons in

a year. Many reasons for overseas tourists preferring

Udhagamandalam are attributed to this.


252 A Geo-Technical assessment of Slope stability condition at Lovedale Club slide,




The socio-political climate in Kashmir and other places

is not conducive for a peaceful holidaying.

Most of the hill stations in Himalayan ranges have

witnessed substantial degradation of environment.

The climate conditions of other hill stations vary to a

great extent and extreme climate and snowfall witnessed

frequently in Himalayan hill station.

The Nilgiris hill is facing two major problems – Natural

and Anthropogenic activities. Urbanization is due to

mainly construction of Resorts, Tourist flux and

intensive agricultural activities. These are all

anthropogenic activities induced soil erosions and slope

instability. The natural forces mainly rain fall

sublimated to above said problems.

Mr. D. Ram Raj from DNA agency in Chennai has

published news on November 11th

2009(Anon, 2009).

He quoted that heavy rain triggered a series of

landslides in Ooty, Conoor, and Kothagiri regions of the

Niligiris. Most of the people were killed after the

landslides slammed to their houses. After 1978, this is

the biggest rain-related disaster in the district. It rained

continuously since November 8, resulting in huge

damage to life and property. Ms. Shika recorded;

landslips and heavy rain claimed more than 29 more

lives in the hill strict, taking the total to 43 in last two

days. Landslides claimed the lives of nine people in

Ooty town, two persons died in the present study area

and it is recorded 170mm rain in 24 hours.

In Ooty municipality, 2009 rain fall about 25 locations

as landslides were occurred in different scales. The

present study has taken in Lovedale village to NH 67

road. It is shown in Figure 1. Out of 25 landslides, 5

slides are occurred in this ghat section. The middle slide

has adjacent to the Lovedale club and taken for detailed

study.

2. Study Area:

The ghat section is connecting NH 67 - Lovedale

village. It is starting 76o 43’07” E to 11

o 23’ 22” N in

NH junction and 76o 42’ 22” E to 11

o 22’ 59” N in

Lovedale village. The GPS co-ordinates of these five

slides are furnished in Table 1. The middle slide is

chosen for detailed study and it is about 750m from the

NH junction. The slope present in the ghat section is

slopping in SE direction slope. The total length of the

slope is about 984m and elevation difference from top

to bottom of the slope is about 360m. The middle slope

Nilgiris Mountain Railways, Mettupalaiyam to Ooty

railway track is present and lower most end one more

road ghat section is present after that the slope is end

with a stream. Geologically the whole area is covered

by charnockitic rock. These rocks are exposed in upper

most/top of the hill and some out crops also seen in the

hill slopes. The predominant soil present in this slope is

reddish brown soil. It is extended up to 4 to 5 m, which

porous in nature. As per the land use/land cover

categories, the upper most slope is barren rocky and

resorts, club and settlements are located and then

followed by tea plantation and agricultural land.

3. Methods used:

For this study, two methods are chose for calculating

stability of the slope as limit equilibrium method under

static condition to calculate factor of safety given by

Coates (1970) and Circular Failure Chart (CFC method)

method proposed by Hoke and Bray (1981). In the case

of homogeneous soil, the shape of the critical failure

surface is assumed to be circular or that of a logarithmic

spiral; the Limiting equilibrium method is widely used

in design of excavation and road cutting in ghat

sections. The accuracy of an equilibrium analysis of

slope stability depends on the accuracy with which the

strength properties and geometric condition of the soil

(Duncan and Wright, 1980). There have been many

studies carried out on slope stability analysis, using

limit equilibrium, Bishop (1955), Jambu (1957),

Fredlund (1984), for geotechnical study, to calculate

factor of safety has also been applied to slope staility

analysis by Hoke and Bray (1981). As a new approach

to using slope stability analysis, software has been

applied and find out the factor of safety by Singh and

Monjezi (2002), and Pourkhosravani and Kalantari

(2011), Pan,et al.,(2012), Pietruszczak and Haghighat,

(2013).

4. Slope stability assessment of Lovedale Club slide

area:

Lovedale – NH 67 ghat section is about 2,194m length.

The Lovedale club is present almost middle of the ghat

section. The slope is dividing into three zones for the

convinent study. Above this Lovedale-NH road is upper

slope, ghat section to NMR railway track is middle and

below the track is a lower slope. The slope is facing to

Achchinakal village; the average slope is 45o with slope

direction towards SE. The stability analysis of this

failed slope is carried out under dry, partial and fully

saturated condition in limit equilibrium method and five

different conditions in CFC method.

A systematic study is carried out and assesses the

stability condition of failed slope and general slope.

Ground investigation was carried out and general profile

of this slope was done (Figure 2) using DGPS from

ridge to bottom of the slope. Wherever the very steep

slope is present, it is consists of loose overburden

having shallow thickness; it is create talus failure and

most potential for existing slope. A geotechnical study,

nine surface samples and three core cutter samples for

each slope were collected; two surface and one core

sample were collected in the slide body at different

253 E SARANATHAN, SUGANYA KANAGASABAI, M KANNAN AND

G K VENKATRAMAN



heights. The co-ordinate of surface sample locations is

shown in Table 2.

The soil properties like unit weight, moisture content

and porosity was calculated to three core cutter samples

and furnished in Table 3, Table 4, and Table 5

respectively. Direct Shear Test was carried out in

geotechnical laboratory to calculate their shear strength

parameters for all samples. Normally five different

normal loads to get their corresponding shear strength

values obtained in each nine samples. The values of

normal Stress ( ) and shear stress (ι) were plotted in

graph sheet X and Y axis and possible combinations

derived from best fit lines of shear test results. The

angle made by the straight line with the horizontal is

(friction angle) and the intercept which the straight line

makes with the vertical axis is the extent of cohesion (c)

calculated. The normal load, shear strength value and

corresponding c and values are shown in Table 6. The

stability condition of the given slope is estimated by

calculating FOS following limit equilibrium method

under static condition (Coates, 1970) for dry, partial

(50%), and complete saturation (100%) condition and

Circular Failure Chart method, FOS is calculated for

dry, 25%, 50%, 75% and 100% saturation condition.

4.1. Limit Equilibrium Method:

The talus slide stability was calculated by Coates (1970)

and the equation of FOS is given below: and Figure 3

shows schematic condition under which the below

equation is established.

F = c.sec2f / .Z + tan [1-(1-Zw / Z). w /] / tanf -(1)

Where,

c = cohesion, = friction angle, = unit weight of slope

forming material, w = unit weight of water, f =

average slope gradient, Z = average depth of overburden

and Zw = average depth of phereatic surface from slope

face. Zw = Z (for dry condition), = 0.5 Z (for partial

saturation condition) and = 0 (for complete saturated

condition).

Lovedale club slope stability carried out using equation

(1). The input data used in this analysis given Table 7.

Resulting of stability analysis using eq. 1 under dry,

partial, and complete saturation in natural slope and

average slope and FOS was calculated Table 8 shows

furnished FOS for three zones.

Fig3: Schematic condition of a Talus Failure indicating

the stresses acting on debris mass. Note: Slope surface

and Slope angle are considered parallel in the analysis

(Anbagalan et al. 2007)

4.2. Circular Failure Chart Method:

The Circular Failure Chart was produce by means of a

Hewlett-Packard 9100B calculator with graph plotting

facilities. This software was programmed to seek out the

most critical combination of failure surface on tension

cracks for each of a range of slope geometrics and

groundwater conditions provision was made for the

tension crack to be located either in upper slope or of

face of the concerned slopes. The CFC method was

proposed by Hoek and Bray in 1981. This is a rapid

method for stability analysis and even beginners may

find it easy to adopt. The main assumptions of the CFC

methods are:

Slope is to be homogeneous.

The shear strength of the materials is characterized by a

c and .

The failure surface to be circular.

A vertical tension crack may be present in upper slope

or face of the cut slope.

A range of groundwater conditions are considered for

the study ie. Dry to fully saturated condition.

In order to account for pore water pressure in subsoil

and forces due to water present in tension cracks, a

serious of groundwater flow pattern are assumed (Hoek

and Bray, 1981). For serious of possible field conditions

have been chosen, which have been indicated in a

combined form as shown in Figure 4. For the safety

purpose condition five groundwater conditions taken for

this study.

The outline of the curve (Figure 5) and steps taken to

find out FOS is given below.

Step 1 – Decide upon the groundwater condition.

Step 2 – Calculate the dimensionless ration c / H.

Tan. Find this value on outer circular scale of the chart.





Step 3 – Follow the radial line from the value found step

2 to it’s inter section with the curve which

corresponding to the slope angle.

Step 4 – Find corresponding value of Tan/F or c/HF.

Step 5 – Calculate the factor of safety.

The present study area, two tension cracks are noticed

near the Lovedale Club building and another one just 50

to 100m away from the previous crack (Plate I). The

upper slope was fully satisfied the CFC condition. The

parameter has taken for this analysis is given in Table 9.

According to the ground condition, the FOS is

calculated for five different groundwater conditions and

the results are tabulated (Table 10).

5. Results and Discussion:

The Ooty town is situated in intermountain valley of

Nilgiris mountain. The soil thickness is marginally high

in this area. Wherever road sections are present, it is

long distance linear structure. It is constructed fast and

in progress of construction inadequately incorporates

geological and geo-technical parameters. Normally in

this slope sections medium to large scale slips are occur

very close to human settlement. In this study clearly

show a present status of Lovedale slide area. The slope

was failed in November 2009 heavy precipitation. The

whole slope including the failed slope has been taken up

for detailed slope stability analysis. The analysis has

been carried out in liquid equilibrium method static

condition and Circular Failure Chart method in different

saturated conditions. Table 8 and Table 10 clearly seen

that Factor of safety value decrease with increase

saturation conditions. The Figure 6a, b, and c show that

FOS vs condition respectively. The results show that in

dry condition, the slope is critically stable while

increasing the saturated condition it becomes unstable.

The both methods results clearly indicated that the

upper slope more unstable than the middle and lower

slope. The results are given in Table 8.

6. Conclusion:

In Lovedale Club slide area, geo-technical study results

indicated that moisture content of the soil is higher

(0.07) than middle (0.05) and lower slope (0.06), it is

clearly indicate the soil ready to prone to slide. In

porosity values is less (0.29) than middle (0.35) and

lower (0.38) area, it is shows that in monsoon season

upper slope got easily saturated compare to middle and

lower zones. The Unit weight is higher (17.71) than

middle (16.02) and lower slopes (15.40), it is seen that

whenever the upper slope got saturated and unit weight

is high, it is very easily prone to slip. The Factor of

safety was calculated by using limit equilibrium and

CFC methods. As per the slope stability analysis by

Circular failure chart method, the slide zone is critically

stable in dry condition. The Factor of safety is just one

(1.10). It is continuously decrease with increase the

water saturation condition. In fully saturated condition,

the Factor of safety was less than one (0.925). The limit

equilibrium method analysis indicated that the Factor of

safety was 0.70, 0.61 and 0.52 in dry, partial and

completely saturated condition respectively in average

slope. In natural slope condition, the Factor of safety

was 0.73, 0.59 and 0.45 for dry, partial and saturated

condition respectively. Under such condition, the toe of

the slides slope should be suitably prevented by

retaining wall or gabion wall.

7. Reference:

[1] Amin Pourkhosravani and Behzad Kalantari,

2011, a Review of Current Methods for Slope

Stability Evaluation, EJGE, Vol.16, pp 1245 –

1254.

[2] Anon 2006, 26th

November 2006, Thinakaran

paper, Tiruchy edition.

[3] Anon 2009, 16th

October 2009, The Hindu paper,

Tiruchy edition.

[4] Anon 2007, 25th

October 2007, The Hindu paper,

Tiruchy edition.

[5] Anon, 2009, www.dnaindia.com.

[6] Bishop, A. W., 1955. The use of the slip circle in

the stability analysis of slopes, Geotechnique, v.5,

pp.7-17.

[7] Chakraborty, D, Anbalagan, R and Kohli, A, 2008,

An engineering geological appraisal of slope

stability condition at D.S.B. College site on

Ayarpata hills in Nainital, Uttarakhand, Landslide

Management – Present Scenario & Future

Directions, CBRI, Roorkee, Proceeding of CBRI

Diamond Jubilee Conference, Feb 10 – 12, pp 157

– 166.

[8] Coates, D.F., 1970. Rock Mechanical Principle.

Department of Energy, Mines and Resources,

Monograph 874, Canada, Chapter 6.

[9] Duncan, J.M., and Wright, S.G., 1980. The

accuracy of Equillibrium method of slope stability

analysis, Engineering Geology, 16, 5-17.

[10] Fredlund, D.G, 1984, Analytical Methods for Slope

Stability Analysis, Proceeding of the Fourth

International Symposium on Landslide, State-of-

the-Art, Sep. 16 – 21, Toronto, Canada, pp 229 –

250.

[11] Hoek, E and Bray, J.W. 1981, Rock Slope

Engineering (Revised Third Edition), E & FN

SPON Publishers.

[12] Janbu, N, 1957, Earth pressures and bearing

capacity calculations by generalized procedure of

slices, Proceeding in International conference of

soil Mech. Foundation Engg. 4th

London, 2: 207 –

212.

[13] Pietruszczak, s and Haghighat, E, 2013,

Assessment of slope stability in cohesive soil due to

http://www.dnaindia.com/


G K VENKATRAMAN



rainfall – International journal of numerical and

analytical model in Geomatics, vol 37, No.18, pp

3278-3292.

[14] Pan,x, Yang, L, ZLang, S, Wei ,P and Sun, M,

2012, Three – dimensional slope stability methods

based on GIS technology, Vol 594-597, pp 2356-

2360.

[15] Singh, T. N., and Monjezi, M, 2002, Slope

Instabvility in jointed Rock mass – A Numerical

Approac, Mining Engineering Journal, Vol.1 (10),

pp 12 – 13.

Fig1: Location map

Fig2: Slope Profile in Lovedale Club





Fig4: CFC groundwater flow condition (Hock and Brey, 1981)

Fig5: Calculation of Factor of Safety from Circular Failure Chart

Fig6a:

Fig6b:


G K VENKATRAMAN



Fig6c:

Table1: GPS location of slide points

Sl. No. Longitude Latitude

1. 76o 42’ 56” E 11

o 23’ 10” N

2. 76o 44’ 04” E 11

o 23’ 39” N

3. 76o 43’ 02” E 11

o 23’ 58” N

4. 76o 42’ 59” E 11

o 23’ 59” N

5. 76o 42’ 58” E 11

o 24’ 03” N

Table2: Surface soil samples GPS Co-ordinates

Sl.No. Sample location Northing Easting Elevation in m

1. Upper S1 11o 23’ 11” N 76

o 43’ 00” E 2272.6

2. Upper S2 11o 23’ 12” N 76

o 42’ 59” E 2271.1

3. Middle S1 11o 23’ 11” N 76

o 42’ 59” E 2261.46

4. Middle S2 11o 23’ 11” N 76

o 42’ 58” E 2258.71

5. Middle S3 11o 23’ 09” N 76

o 42’ 59” E 2232.2

6. Lower S1 11o 23’ 06” N 76

o 43’ 59” E 2175.7

7. Lower S2 11o 23’ 06” N 76

o 43’ 00” E 2170.87

8. Lower S3 11o 23’ 05” N 76

o 43’ 00” E 2167.43

9. Lower S4 11o 22’ 59” N 76

o 43’ 04” E 2090.34

Table3: Unit Weight of core samples

Upper Middle Lower Average

Mass of the core cutter W1 (g) 920 950 1022 964

Mass of the core cutter + soil

W2 (g) 2780 2644 2654 2693

Unit weight (kN/M^3) = (W2 -

W1/ V 18.60 17.14 16.32 17.35

Dry unit weight (kN/M^3) D

= /1 + W) 17.71 16.02 15.40 16.38

Table4: Moisture content in core samples

Upper Middle Lower Average

Weight of can. W1 (g) 920 950 1022 964

Weight of Can. + wed soil W2 (g) 2780 2644 2654 2693

Weight of Can. + wed soil W3 (g) 2692 2552 2560 2601

Water/Moisture content

W (%) = [(W2 - W3)/( W3 – W1)] x 100 0.07 0.05 0.06 0.06

Table5: Detailed Geotechnical results of core samples

Upper Middle Lower

Specific gravity G 2.67 2.69 2.67

Unit weight (kg/m3) 18.6 17.14 16.32

Void ratio e 0.41 0.54 0.60

Porosity n 0.29 0.35 0.38

Table6: Direct Shear Test results





Location Normal load

in kg/cm2

Normal stress in

KN/m2

Shear load in

Kn

Shear load

in KN/m2

Cohesion of soil

in KN/m2

Angle of internal

friction in degree

Upper S1

0.5 49.05 0.09 25

20 31

1.0 98.1 0.1584 44

1.5 147.15 0.396 110

2.0 196.2 0.5148 143

2.5 245.25 0.612 170

Upper S2

0.5 49.05 0.1728 48

50 30

1.0 98.1 0.378 105

1.5 147.15 0.468 130

2.0 196.2 0.5868 163

2.5 245.25 0.7668 213

Middle S1

0.5 49.05 0.2232 62

20 38.5

1.0 98.1 0.2592 72

1.5 147.15 0.4932 137

2.0 196.2 0.6696 186

2.5 245.25 0.7488 208

Middle S2

0.5 49.05 0.1944 54

20 39.5

1.0 98.1 0.3672 102

1.5 147.15 0.4536 126

2.0 196.2 0.6192 172

2.5 245.25 0.828 230

Middle S3

0.5 49.05 0.252 70

35 36.19

1.0 98.1 0.4032 112

1.5 147.15 0.5328 148

2.0 196.2 0.6696 186

2.5 245.25 0.972 270

Lower S1

0.5 49.05 0.288 80

40 34.98

1.0 98.1 0.4032 112

1.5 147.15 0.504 140

2.0 196.2 0.6408 178

2.5 245.25 0.792 220

Lower S2

0.5 49.05 0.216 60

30 37.3

1.0 98.1 0.3528 98

1.5 147.15 0.5328 148

2.0 196.2 0.6408 178

2.5 245.25 0.828 230

Lower S3

0.5 49.05 0.1512 42

10 38.37

1.0 98.1 0.3744 104

1.5 147.15 0.4752 132

2.0 196.2 0.5616 156

2.5 245.25 0.7344 204

Lower S4

0.5 49.05 0.2664 74

12 48

1.0 98.1 0.4032 112

1.5 147.15 0.648 180

2.0 196.2 0.7848 218

2.5 245.25 0.9468 263

Table7: Soil input parameter for Liquid Equilibrium analysis


G K VENKATRAMAN



Parameter Upper Middle Lower

Dry 50% 100% Dry 50% 100% Dry 50% 100%

Soil thickness

(D) 6 6 6 8 8 8 7 7 7

Surface

inclination (f) 48 48 48 38 38 38 42 42 42

Unit Weight of

soil () 18.6 18.6 18.6 17.14 17.14 17.14 16.32 16.32 16.32

Friction angle

() 30o30’ 30o30’ 30o30’ 38o3’36” 38o3’36” 38o3’36” 39o39’36” 39o39’36” 39o39’36”

Cohesion (c) 25.0 25.0 25.0 25.0 25.0 25.0 23.0 23.0 23.0

Unit weight of

water (w ) 9.81 9.81 9.81 9.81 9.81 9.81 9.81 9.81 9.81

phereatic

surface from

slope face (Zw)

6 3 0 8 4 0 7 3.5 0

Table8: FOS for soil sections in different groundwater condition Liquid Equilibrium Method

Soil section

Factor of Safety

Average slope

Factor of Safety

Natural slope

Dry Partial Complete Dry Partial Complete

Upper 0.70 0.61 052 0.73 0.59 0.45

Middle 0.77 0.57 0.37 0.95 0.66 0.38

Lower 0.81 0.61 0.41 0.96 0.68 0.40

Table9: Soil input parameters for CFC analysis

Soil property Upper

Slope angle 48o

Height of slope 10

Density of soil 18.6

cohesion 25.0

Internal friction angle 30o30’

Dimensionless ratio 0.23

Table10: F value of the slope for different groundwater flow condition

FOS (F) as per the X

and Y intercept of the

chart

Groundwater flow condition

Chart 1

Dry

Chart 2

25%

Chart 3

50%

Chart 4

75%

Chart 5

saturated

Upper Slope

X Intercept (F1) 1.09 1.075 1.018 1.00 0.92

Y Intercept (F2) 1.11 1.11 1.09 1.016 0.93

FOS avg = (F1 +F2)/2 1.10 1.09 1.05 1.00 0.925

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ISSN 0974-5904, Volume 07, No. 01

February 2014, P.P.260-268


Land-Slide Hazards of October 2009 at Karwar, Karnataka: A

Lesson for Planning Developmental Activities in the Tropical Ghat

Regions

V S HEGDE1, KRISHNAPRASAD P A

2, SHALINI R

3, DEEPMALA NILAMWAR

1, TEJASWINI B

1,

GIRISH K H4 AND C S MALEWADI

1

1SDM College of Engineering and Technology, Dharwad 580002, INDIA

2SDM Institute of Technology, Ujire D.K. 574240, INDIA

3Global Academy of Technology, Rajarajeshwari Nagar, Bangalore 5800098, INDIA

4Government Engineering College, Haveri, INDIA


Abstract: Landslides of October 2009 at Karwar caused the death of 19 people, destroyed 5 houses completely and

damaged several other houses, national highway and the Karwar port. These are considered the most severe ones

among recent landslides. Around the same time in 21 locations, landslides occurred. A study of geomorphologic,

geologic, hydrographic, and land use/ land cover pattern of 1500 sq Km around Karwar area based on the satellite

data to understand the causes of the landslides was conducted. Temporal and spatial distribution of the landslides

suggest the role of active tectonics that have been triggered by human interference such as deforestation, fragile

slopes modification, blocked natural drainage, unscientific quarrying, and land use practices due to many project-

related activities such as Sea Bird at Karwar, Kadra dam project, Kaiga Nuclear power projects, Konkan railway

etc. The study indicates that in the areas of tectonically active and environmentally fragile mountain regions like the

Western Ghat, before planning any project, knowledge of the hydro-geomorphologic, geophysical,

geoenvironmental and tectonic history of the region is critical.

Keywords: Landslides, Western Ghat, Tropical region, Active Tectonics, Anthropogenic activity.

1. Introduction:

Land Slides are common hazards in mountainous

regions especially of high rainfall areas of tropical

regions where weathering is deep (Sajinkumar et al.,

2011) like in the Western Ghats. Direct relation between

effect of weathering and slope failure resulting in land

slide at the foot of the scarp are well known (Pasto and

Silvano, 1998, Gupte et al., 2013). Although, slope

failures are not uncommon in hilly regions in tropical

countries, the severity of the landslides that occurred in

October, 2009 at Karwar, Karnataka, are considered the

worst in recent times. These landslides caused the death

of 19 people, destroyed 5 houses completely, damaged

several others and caused huge loss of property to the

National High way, Karwar fishery port etc. Many

project-related activities like deforestation, blockage of

natural drainages due to the activities of Sea Bird

project, quarrying, Kadra dam project, Kaiga Nuclear

power projects, Konkan railway etc are believed to have

triggered the slide, and aroused much interest among the

scientists and public. In this paper an attempt has been

made to understand the causes for the slide based on

field observation and satellite data interpretation.

Conventional approach of land slide study involves

investigations of geotechnical properties of the soil such

as internal friction, cohesion, thickness of the soil cover

etc. When large areas with diverse types of slides and

material are involved as in the case of Karwar,

geotechnical approach becomes impracticable (Sambhu

et al., 1997). In large areas causative factors vary, so

geological and geomorphological approach are more

useful (Sambhu et al., 1997; Pitchai Muthu and

Muralidharan, 2005). Therefore, in this paper an attempt

has been made to understand the causes for the slide

based on multiple approaches like geomorphological

and geological studies, followed by field observation

and geotechnical approach. All the field and satellite

derived data have been processed in Geographical

Information System (GIS) platform, and causes for the

landslide have been inferred.

1.1. Study Area:

The study area is located in the tropical belt at the foot

of the Western Ghat at Karwar (Latitude 14042’14”N to

14054’46”N and Longitude 74

005’14”E to 74

020’57”E).



261 V S HEGDE, KRISHNAPRASAD P A, SHALINI R, DEEPMALA NILAMWAR,

TEJASWINI B, GIRISH K H AND C S MALEWADI



Fig1: Location map of the study area shown in Satellite image of Karwar area. Also note major lineaments in the

area, stream ponding and obtuse bend at Bargadde near Karwar.

The study area covers about 1500 sq Km around

Karwar, which is a hilly coastal tract. It receives annual

rainfall of ~300 cm of which 85 % falls between June

and September.

Karwar is a small town, but with many Industrial

activities like Kadra hydroelectric power project (35

Km. from Karwar), Kaiga nuclear power Project (43

Km. from Karwar), Sea Bird project at Karwar and

Karwar Port etc. The Konkan railway line crosses

Karwar through a tunnel (Fig.1). A large number of

quarries are being operated around Karwar especially

close to the Highway between Karwar and Ankola.

1.1.1. The Disaster:

Landslide calamity at Karwar took place on 2nd

October 2009 between 4 and 4.20 pm in 21 locations.

Two slides near Kadwad, one near Karwar town, one

at Karwar fishery port and 17 along the national

highway between Karwar and Belekeri (Fig.1) (Table

1). The slides at Karwar port and Kadwad were soil

slides; near Karwar town it was rock fall and along

the Highway a mix of soil, weathered debris and

boulders. At Kadwad as people reported, hill slide

occurred suddenly from 268 m above msl, to 15 m,

and it moved laterally ~150m in just 3 to 4 minutes

(Fig.2). The people reported that, there was not even

time for them to come out of their houses; five houses

along with many coconut trees were completely

destroyed and buried under the thick soil cover of the

slide materials. One house moved laterally ~100m

from its original place and collapsed. It is estimated

that at Kadwad, from an Overburden area of

~190mX100mX50m soil was dislodged causing death

of 19 people apart from huge loss of property. Along

the highway, slide consisting of boulders, debris, rock

fragments and weathered materials that fanned out to

a wider area at the location were observed (Fig.3).

Before the tragedy occurred, it had rained for 3

consecutive days cumulating in heavy precipitation of

43.3cm. Historical records of landslides and multidate

images suggest that landslides have occurred

repeatedly in some locations which are in proximity

to the major lineaments in the area (Fig.4).

2. Regional Geology and Geomorphology:

Geologically, the coastal belt near Karwar and the

Western Ghats are dominated by Archaean granites,

laterised variably. This area underwent deep chemical

weathering resulting in thick mantle of lateritic soil

cover which is a common feature in tropical countries.

Tertiary and modern sands are found along the narrow

262 Land-Slide Hazards of October 2009 at Karwar, Karnataka: A Lesson for Planning




coastal strips. A number of lineaments have been

detected in the satellite images based on various image

processing techniques followed by geomorphic features

(Fig.1).

Fig2: a) Photograph of the Kadawad site after land

slide. b) Schematic sketch of landslide at Kadwad,

Karwar.

Drainage is dendritic and drainage density is high, but

higher order streams are structurally controlled. Plot of

drainage order number verses number of drainages in

each order indicate first order streams are more in

length than the normal (Fig.5). Lower order streams are

the youngest component of the drainage network and

their preferred orientations are related to recent active

tectonic phase (Centamore et al., 1996). Development of

more number of first order streams imply that the region

is being in active tectonic region and upliftment.

Raised old beaches at several locations along with wave

cut platforms are observed in this area which provides

evidences for higher palaeo sea-levels while islands,

cliffed shorelines without beaches and drowned river

mouths indicate shoreline submergence. The Western

Ghat forms a wall like scarp on the east and a narrow

coastal plain on the west. A cross profile from west to

east shows a sudden rise especially near Karwar (Fig.

6). This wall-like rise and origin of the Western Ghat

has been explained by many geologists as due to

faulting (Subramanya, 1998). Apart from these, the

headlands jutting into the sea with faulted scarp-like

formation on one side perpendicular to the coast (see for

ex Fig.7, near Honnavar,~85km south of Karwar, not

shown in the map) are indicative of tectonically

controlled geomorphic features. A Tributary of the

River Kali has anomalies in its flow path from NW to

SE, initially flowing against the main direction of flow

and joins the Kali with a sand deposit near Bargadde at

the confluence point (Fig.1a). It developed a

meandering path despite its gradient in that stretch.

Also, palaeo river features can be seen in the satellite

images. In the mouth of the Kali and the Gangavali

(Fig.8a and b) deposition of the sand resulting in a

bar/spit formation is observed which indicates

aggradations process. There are many Island-like

features in the estuary which corroborate the process of

aggradations. The river shows abrupt turning in the

direction of the fault in a tributary of the Kali near

Karwar, along with upstream pounding and downstream

incision with respect to the lineament (Fig.1). River

bending, formation of islands and swampy condition in

swift flow region of the Western Ghat to coastal belt are

believed to be the manifestation of the active tectonics

(Marple and Talwani, 1993). Near Belikeri, pebble beds

and terraces are observed. These geomorphic features

suggest uplift of the downstream block. Development of

these geomorphic features in Quaternary sedimentary

environment (spits, island within estuary entrenching

and pebble beds in the coastal belt) can be cited as the

evidence of neotectonism (c.f.Holbrook and Schumm,

1999), which imply that the area is seismically active.

Fig3: Photograph showing Landslide along the

Highway, near Karwar consisting of rock boulders,

weathered rocks and soil etc.





Fig4: Landsat-ETM image of the Karwar area showing lineaments, and locations of recent landslides; Google earth

images showing location of landslides. Arrow indicates direction of movement of earth material. Note that earth

material moved away from the landslides.

Fig5: Plot of drainage order v/s number of drainages

in each order of the study area

2.1. Seismicity:

Earlier workers (Raval, 1995) reported negative gravity

anomaly of the order of 70 and 120 Mg. in general for

the region across the Western Dharwar craton that

covers the Western Ghats of Karnataka. Low level of

strain accumulation (10< neon strain/year) has been

reported for the whole of South India (Paul el al., 1995).

This implies that there is relaxation of accumulated

strain. The principle stress analysis of stress values of

N. 40°W (Gowd et al., 1996) by Valdiya (2001)

indicated strike slip movement between 12-16° in

Dharwar craton and North Westerly compression.

Therefore it is probable that these fault systems have

facilitated the stress relaxation. Distribution of historical

seismic data is suggesting that this region lies in the

high seismic intensity area (Fig.9). Therefore, some of

the faults could be seismogenic and tectonically active.





Fig6: Topographic profiles of land forms Across Karwar and Belekeri region (see Figure. 1 for the transect

details). Note that cross-profiles cross lineaments and along the slope landslide has occurred.

Fig7: Coast perpendicular fault with faulted scarp-like

on southern side at Kasarkod, Honnavar.

2.2. Climate, Rain fall and soil type:

Climate and rain fall are the main determining factors of

both weathering and landslide (Buma, and Dehn, 1998).

The studied area being situated in tropical climate and

high temperature, chemical weathering is deep. The area

is bordered by Western Ghats on the east which rises

like a wall and act as an effective barrier for rain

bearing clouds arriving from the West and causing high

orographic rain fall in the area ranging from 300 to

400cm.

Table1: Locations of some important landslides at Karwar and soil mass dislodged

Sl. No. Name of the location No. of slides Mass dislodged in m3 Type of material

1 Kadwad (Zariwad) 1 9,50,000 Soil

2. Kadwad 1 4,00,000 Soil

3 Karwar port 5 90,000 Soil

4 Near Karwar town 1 Not estimated Rock fall

5 High way 3 ~35,000 Mix of soil and

boulder





Table2: Selected physical properties of the soil from slide area, Kadwad, Karwar

Samples Density

KN/m3

Specific

Gravity

Cohesion

(Kg/cm3)

Friction Safe Bearing Capacity

(KN/m3)

Highway 14.45 2.31 0.300 110 180

Karwar port 13.73 2.26 0.510 130 143

Kadwad 16.87 2.35 0.413 10.70 160

Fig8: Spit across the Gangavali river near Ankola (a) (The river is 24 km south of Karwar, not shown in the map)

and submerged bar in front of the Kali river mouth (b) indicating the aggradation processes.

Percolation of the water increases the pore pressure, and

removes soluble constituents from the soil (Jenny,

1980). It reduces the cohesive forces which are

triggering factors of landslides.

2.3. Anthropogenic activities:

Many studies have demonstrated that anthropogenic

activities, in particular deforestation leads to drastic

increase in probability of landslides (c.f. Derose et al.,

2006). Due to deforestation, soil cohesion decreases,

which results in higher rate of mass movement and

sediment delivery leading to slope failure in hilly areas.

In the study area due to many projects, large scale

change in land-use /land cover pattern have been

observed. Extensive quarrying, the Konkan railway

tunnels, Sea Bird Projects, Dam construction and

Reservoir formation and the resulting urbanization are

all believed to contribute to the land use pattern

changes. Superimposition of the Normalized Difference

Vegetation Index map of 1990 and 2000 generated

based on Land sat TM and ETM+ suggest significant

forest cover loss during the above period (Fig.10). Few

of the landslides have occurred in this deforested area.

Comparison of the drainage as seen in the Toposheet

(surveyed during 1976 and 1979) and the present, shows

that natural drainages have been modified (Fig.11)

Construction for railway track at Kadwad, near Karwar

has blocked the natural drainage, and along the National

High-way between Binaga and Karwar, a barrier wall-

constructions to prevent water entry into the project area

have modified the normal route of the drainage in the

area. All this human interference may have played a role

in triggering the natural forces. Especially, vibration

caused due to movement of train (Fig. 1) and blasting

for quarry work may have aggravated the sliding. Water

percolating along the fracture/shear/fault zones which

lubricates the sliding contacts may have facilitated the

landslides.

Geotechnical properties such as cohesive force, bulk

density and shear parameters provide information on the

soil characteristics and hence slide prone areas

(Anbazhagan et al., 2010). The materials involved in the

slide, especially at Kadwad and Karwar port are mostly

soils that have very low bulk density, cohesion and

shear values (Table 2) signifying dominance of clay

fraction. Low density implies high porosity while, on

percolation, water acting as lubricating effect reduces

the frictional forces further down, favoring sliding. Here

the nature of the slide initially from top, and later

horizontal movement and displacement of the collapsed

house suggest complex sliding mechanism. Both at

Kadwad and Karwar port, ground is flat immediately

after the slop. Movement of overburden material

towards valley/dip side is determined by the geometry

of the floor, and this may prove to be one of the





important contributors to instability (Kasmer et al.,

2006). As the material in the zone vicinity of toe is

subjected to high level of stresses, reduction of material

shear strength in the zone takes place. This

consequently forms a weak zone of crushed material

which is a favorable situation for planar failure (Gupte

et al., 2013). On the other hand, from the top, circular

failure may initiate. The toe undergoes translational

movement parallel to the base of the dump floor. This

results in complex failure consisting of a circular sliding

surface passing through the dump material at the upper

part of the dump and a planar surface along the interface

between overburden material and dump floor (Gupte et

al., 2013). Thus, two different modes of failure have

taken place resulting in compound failure.

Fig9: Seismic zonation map of the study area prepared based on the historical data for the region (Valdiya 2001).

Due to thick weathered soil, thick forest, water

resources (both rain fall and drainages) and

mountainous topography, tropical Ghat regions are

environmentally fragile. To harness water resources and

related project in the mountainous topography compels

many infrastructure developmental activities along the

coast and foot of the mountains in tropical belt. As these

mountains are tectonically active apart from

environmentally fragile, the area is vulnerable to natural

catastrophy like Earth-quakes and landslides. The

integration of the geophysical data and

geomorphological features along with the spatial

association of the present and past landslides suggest

that they are tectonically controlled. Occurrence of

landslides along the steep slopes that has suffered

deforestation; high rain fall and soil mass involved

indicate that landslides are triggered by anthropogenic

activity.

Fig10: Normalized Difference vegetation Index (NDVI) map prepared from Thematic Mapper image of 1990 (a)

and Enhanced Thematic Mapper image 2000(b) of Landsat series, showing change in vegetation pattern and areas

of deforestation around Karwar area





Fig11: comparison of Toposheet of 1976 and Google earth Image of 2009,

Therefore while planning for any project, the

geomorphologic, geophysical and geoenvironmental

investigations and tectonic history of the region is

critical in relation to the scale and nature of the project.


The present work is carried out through the facility

generated under the ISRO’s special assistance grant

scheme (No. B. 19014/5/2009-II), Authors are sincerely

thankful to the ISRO for the grants. Authors thank the

authorities of the SDMCET, Dharwad for the

encouragement to carry out the present work. Authors

thank sincerely the reviewers of the paper whose

suggestions and comments helped a lot to improve the

quality of the paper.

4. Reference:

[1] S. Anbazhagan, S.K. Sajinkumar, and T.N. Singh

(2011), Remote sensing and geotechnical studies

for slope failure assessment in part of Ernakulam

and Idukki District, Kerala, India. In: T.N. Singh

and Y.C. Sharma (Eds.), Slope stability (Natural

and Man Made Slope), Vayu Education of India,

New Delhi, pp.255-281.

[2] J. Buma, and M. Dehn, (1998), a method for

predicting the impact of climate change on slope

stability, Environ. Geol., 35, pp. 190-193.

[3] E. Centamore, S. Sciacca, M. Montedel, P. Frdi,

and P.E. Lupia, (1996), Morphological and

morphometric approach to the study of the

structural arrangements of northeastern Abruzo

(Central Italy), Geomorph. 16, pp.127-137.

[4] R.C. Derose, N.A. Trustrum, and P.M. Blaschke,

(2006), Post deforestation soil loss from steepland

hillslopes in Taranaki, New Zealand. Earth Surface

Process and Landforms, 11, no. 2, pp.131-144.

[5] T. N Gowd., S.V. Srirama Rao, and K.K. Chary,

(1996), Stress field and seismicity in the Indian

Shield: Effects of the collision between India and

Eurasia, Pure and appl. Geophy, 146, pp.503-531.

[6] S. S. Gupte, Rajesh, Singh, V. Vishal and T. N.

Singh (2013) Detail investigation of stability of in

pit dump slope and its capacity optimization. Int.

Jour. Ear. Sci. and Engg., v. 06, (02) pp. 146-159.

[7] J. Holbrook, and S.A. Schumm, (1999),

Geomorphic and sedimentary response of rivers to

tectonic deformation: A brief review and critique of

tool for recognizing subtle epeirogenic deformation

in modern and ancient setting, Tectonophy, 305,

pp.287-306.

[8] H. Jenny, (1980), Factors in soil formation.

McGraw Hill, New York, 271p.

[9] O. Kasmer, R. Ulsay, C. Gokceoglu, (2006) Spoil

pile instabilities with reference to a strip coal mine

in Turkey: mechanisms & assessment of

deformations Environ. Geol., 49, pp. 570-585.

[10] R.T. Marple, and P. Talwani, (1993), Evidence of

possible tectonic upwarping along the southern

Carolina coastal planes from an examination of

river morphology and elevation data, Geol., 21,

pp.651-654.





[11] A. Pasto, and S. Silvano, (1998), Rainfall as a

trigger of shallow mass movements. A case study in

the dolomites, Italy. Environ. Geol., 35, pp.184-

189.

[12] J. Paul, F. Blume, S. Jade, V. Kumar, P. S. Swathi,

M.B., Ananda and V.K. Gaur, Ronald Burgmann,

Roger Bilham, Namboodri, B. and Dave Mencin

(1995) Microstrain stability of Peninsular India,

1964-1994, Proc. of the Indian Academy of

Science,( Ear. Planet. Sci.), 104, pp.131-146.

[13] R. Pitchai Muthu and C. Muralidharan, (2005)

Causes and Mechanism of Amboori Landslide of

9th November, 2001, Thiruvananthapuram District,

Kerala, Jour. Geol. Soc. India, 66(2), pp.203-208.

[14] U. Raval (1995), on certain large-scale gravity field

patterns over the Indian subcontinent, Proc. Sem.

Space Application in Ear. Syst. Sci., Indian

Geophy. Uni. Hyderabad, pp.153-168.

[15] K. S. Sajinkumar, S. Anbazhagan, A. P. Pradeep

Kumar, A.P. and Rani, V.R. (2011), Weathering

and landslide occurrences in parts of Western

Ghats, Kerala, Jour. Geol. Soc. India, 78, no.3,

pp.249-257.

[16] V. Sambhu, Panikkar and V. Subramanyan, (1997)

Landslide hazard analysis of the area around

Dehradun and Mussoorie, Uttar Pradesh, Current

Science, v.73, no.12, pp.1117-1123.

[17] K. R. Subramanya, (1998) Tectono-magmatic

evolution of the west coast of India, Gond. Res.,

v.1, pp.319–327.

[18] K. S. Valdiya, (2001) River response to continuing

movements and the scarp development in central

Sahyadri and adjoining coastal belt, Jour. Geol.

Soc. India, v.57 pp.13-30.

http://www.springerlink.com/content/0016-7622/

http://www.springerlink.com/content/?Author=K.+S.+Sajinkumar

http://www.springerlink.com/content/?Author=S.+Anbazhagan

http://www.springerlink.com/content/?Author=A.+P.+Pradeepkumar

http://www.springerlink.com/content/?Author=A.+P.+Pradeepkumar

http://www.springerlink.com/content/?Author=V.+R.+Rani

http://www.springerlink.com/content/q81364q4897hh4l8/



http://www.springerlink.com/content/0016-7622/

http://www.springerlink.com/content/0016-7622/78/3/

http://www.sciencedirect.com/science/journal/1342937X

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Indexed in





ISSN 0974-5904, Volume 07, No. 01

February 2014, P.P.269-274


Sedimentary Basin Screening Techniques using Remote Sensing

Bathymetry Data and ArcGIS for Eastern Continental Margin of

India

MRUTYUNJAYA PANIGRAHI1 AND MADHUMITA DAS

2

1E&P, RIL, Ghansoli, Navi Mumbai, Pin-400710

2Department of Geology, Utkal University, Bhubaneswara, Orissa, Pin-751 004


Abstract: Remote Sensing bathymetry which scans the sea floor reflects dominant geomorphological features.

Understanding depositional processes in a deep water environment can be viewed as an important tool for any

exploration. Eastern continental margin of India represents a pericratonic passive margin characterized by various

en-echelon faults running parallel to coast line. This study aims to classify the eastern offshore of India into various

types based on basin configuration. Major geomorphic attributes like slope, azimuth, and flow accumulations are

used to provide the preliminary highlights of quantitative analytical techniques using ArcGIS software with 3D and

spatial analyst tool. Using the GEBCO global bathymetry data, the qualitative classification is adopted along eastern

offshore India margin based on their morphology. Quantifying dimension and spatial variability on regional scale

shall throw light in understanding the basin geology & tectonic process.

Keywords: GIS – Geographic Information System, GEBCO - General Bathymetric Chart of the Oceans.

Introduction:

Remote Sensing bathymetry scans the sea floor for

dominant geomorphological features. The deep water

basin configuration is very important in influencing

deep water sedimentation (Stiffens et al., 2003).

Understanding depositional processes in a deep water

environment is of utmost importance in any exploration.

Eastern continental margin of India represents a

pericratonic passive margin characterized by various en-

echelon faults running parallel to coast line. Deposition

in deep water basins are controlled by many factors like

basin tectonics, sediment supply and relative sea level

changes (Mutti & Normark 1991). This study aims to

classify the eastern offshore of India into various types

based on basin configuration. With the help of GIS

technology as a spatial tool to interpret and analyze

various morphometric parameters from available

present day GEBCO bathymetry image, which is

described in this study. Various geometrical attributes

like dip, slope, azimuth, flow direction, stream orders

and slope profiling are calculated using Arc GIS spatial

tool and are being used for demonstration of shelf,

slope, toe of slope, basinal part and mini basins.

A number of trend surface analyses have been done for

identifying various zones of accommodations e.g.

ponded, healed slope and slope accommodation. This

quantitative analysis is focused on understanding

depositional pattern in the deep water slope system.

This describes the “fill and spill” deposits proposed by

Satterfield & Henrens (1990). Fill & spill describes the

process of intraslope basins filling from updip to

downdip. One important aspect of understanding fill &

spill process is the concept of accommodation. Using

the GEBCO global bathymetry data, a screening and

classification process is adopted along eastern offshore

India margin based on their morphology. Quantifying

dimension and spatial variability on regional scale shall

throw light in understanding the basin geology &

tectonic process.

Data Description:

Present day available GEBCO data for eastern

continental margin are used for the study. Global

bathymetry gridded datasets for the world’s oceans

provided by General Bathymetric Chart of the Oceans

(GEBCO) in 30 arc sec and 1 min grids are freely

available .The datasets for this study area (Fig.-2) is

downloaded from the web link (http://www.bodc.ac.uk).

This moderate resolution of data is suitable for regional

studies. GIS provide faster data processing tools for

visual depiction and analyses of various topographic

derivatives using its inbuilt algorithm. A simple data

processing approach was adopted to convert GEBCO

grid using bathymetry viewer to GIS ASCII format,

which was further organized in ArcGIS environment.

Methodology:



http://www.bodc.ac.uk/

270 Sedimentary Basin Screening Techniques using Remote Sensing Bathymetry Data and




The bathymetry data has been processed using GIS

system in built algorithm and using hydrology spatial

tool various attribute maps have been extracted. The

study was performed in two stages using ArcGIS

software.

Stage-I : Preliminary stage of the study included

preparation of bathymetry maps as well as various

attribute maps like dip, slope, azimuth, flow direction,

stream orders. Few of these maps are described in this

paper.

Stage –II: The second stage includes trend surface

analysis for further classification.

Regional Geology:

The eastern continental margin of India (ECMI)

represents a passive margin set up. A number of

sedimentary basins on onshore as well as offshore are

being described by various authors. These sedimentary

basins are characterized by different tectonic setup as

well as sediment depocenters. Recent discoveries of

giant gas fields in the Krishan- Godavari deepwater off

India’s east coast have put the country on the world’s

“deepwater map.” Exploration has focused on adjacent

deepwater basins in Bengal-Mahanadi in the north,

Cauvery in the south, and the Andaman backarc basins

(Bastia, 2006).

Basin Screening Parameters:

The important geomorphic parameters considered here

in this study are:

Graded Slopes: In general, graded slopes are gentler

with almost no varying topography while above grade

slopes are characterized by presence of step like features

or irregular topographic lows. Generally these slopes are

divided based on the types of accommodation available

on them.

Ponded accommodation: This accommodation occurs

within three dimensionally closed topographic lows

(Prather, 2000). These are the characteristics of salt

dominated basins. Even in the shale withdrawal basins

these are available. So these types of accommodation

results in deposition in a confined basin.

Healed slope accommodation: This occurs in the space

above the stepped equilibrium profile. These deposits

actually wedge out towards the basinal part. Steffens et

al (2003), defined healed slope accommodation in 3D as

the space between top of ponded accommodation and

below a 3D convex hull fit to the rugose seafloor

topography. The same principle is being used in this

study.

Slope accommodation: This is the space between the

highest stable graded-slope angle and the top of healed

slope accommodation. In case of the graded slope, no

ponded accommodation occurs hence there is more

predominance of the healed slope or slope

accommodation. This results in more bypass of

sediments on the upper part and more deposition

towards the basinal side. (Prather 2003).A schematic

diagram showing the various types of accommodation is

presented in Fig-1.

Total space available for deposition is measured by

fitting a 3D convex hull from the shelf slope break to

the toe of the slope. Ponded accommodation is

interpreted where there are three way closing lows.

Healed slope accommodation is the difference between

the total slope and the ponded slope accommodation.

The ponded fill represents sheets and channel levee

deposits (Prather 1998).Once the mini basin is filled,

sediment spills downslope to the lower mini basin.

Above the ponded deposits the downslope basins ate

characterized by muddy flows or pelagic deposits

whereas the up slope basin may contain channels

(Prather 1998).

Analysis & Interpretation:

The regional bathymetry map prepared for the whole of

east coast of India (Fig-2) shows a well-developed

present day shelf and slope system and a vast deep

water area. Shelf in the east coast of India is narrower

with respect to the shelf on western continental margin

of India. A number of present day deep water

channels/canyons can be interpreted from the fig. 2.

These are related to the prominent river systems active

in the east coast of India.

Slope attribute calculated on the data set is presented in

Fig-3. This clearly shows the shelf and slope definitions.

This map can be used to predict the deep water

deposition as well as the bypass zone. Another attribute

map “flow accumulation” is given in fig. 3. This

represents the stream pattern active in the deep water

based on the available data set. It can be seen that the

almost all the streams are west flowing and well

corroborates with the present day river systems. In some

areas few converging streams were found, which refers

to the presence of topographic lows and the diverging

patterns to the topographic highs. This map describes

the overall mass transport from the shelf to the basin

floor.

Ponded accommodation in the east coast of India was

identified by isolating the three way enclosing lows.

Fig-4 depicts the isolated three dimensional topographic

lows identified. As seen from the fig. the northern part

of the area is having sparse ponded accommodation

zones. This area envelopes part of Bengal and Mahanadi

basin and is having huge sediment thickness provided

by the mighty Ganges, Brahmaputra and Mahanadi

rivers. A profile through the area shows presence of a

number of present day cuts. Investigation of the map

271 MRUTYUNJAYA PANIGRAHI AND MADHUMITA DAS



suggests these to be the active canyons. These can act as

conduit for the sediment transport to the deeper water.

The basin’s characteristic feature is its enechelon horst

and graben system which is filled with a thick pile of

sediments of Permian-to-Recent age (Gupta, S.K,

2006).A number of profiles across the east coast of

India are presented in Fig-5 to demonstrate the different

basin configuration interpreted from bathymetry data.

Integrating all the above attributes and studies a

summary map showing the probable depositional zones

off eastern offshore of India is shown in Fig- 6.

Conclusion:

This study explores the potential of screening the deep

water basins into different categories based on basin

configuration using remote sensing data & GIS

technology. The approach facilitates the use of freely

available bathymetry data which are not of very high

resolution. A more detailed study of the high resolution

bathymetry data with seismic signatures will help in a

better understanding of the depositional pattern &

tectonics of this basin. This basin screening

methodology can be used for any basin in the globe.

1. Reference:

[1] Mutti, E., & Normark, W. R. (1991). An integrated

approach to the study of turbidite systems. In P.

Weimer, & M. I. Link (Eds.), Seismic facies and

sedimentary processes of submarine fans and

turbidite systems (p.75–106). New York: Springer.

[2] Prather, B. E. (2003). Controls on reservoir

distribution, architecture and stratigraphic trapping

in slope settings. Marine and Petroleum Geology,

20(6–8), 527–543.

[3] Bastia R. (2006.a). An overview of Indian

Sedimentary Basins with Special Focus on

Emerging East Coast Deep Water Frontiers. The

Leading Edge, July 2006,p 818-829.

[4] Bastia R. (2006.b). Geologic settings and petroleum

systems of India’s east coast offshore basins:

concepts and applications. Dehradun, Technology

Publications, 2007, xvi, p 39, ISBN 81-901767-8-1.

[5] Dewangan, P., Ramprasad, T., Ramana, M. V.,

Mazumdar, A., Desa, M., Badasab, F. (2008). Shale

Tectonics in the Continental Slope and Rise

Regions of Krishna-Godavari Basin, Bay of

Bengal: Implication in Gas-Hydrate Exploration.

American Geophysical Union, Fall Meeting 2008,

abstract OS33A-1313.

[6] Gupta S.K. (2006). Basin architecture and

petroleum system of Krishna Godavari Basin, east

coast of India. The Leading Edge, July 2006,p 830-

837.

[7] Steffens, G. S., Biegert, E. K., & Sumner, H. S.

Bird, D. (2003). Quantitative bathymetric analyses

of selected deepwater siliciclastic margins:

receiving basin configurations for deep water fan

systems. Journal of Marine and Petroleum Geology,

20, p547-561.

[8] L. De Santis, A. Caburlotto, D. Accettella, A. Cova,

M. Presti, F. Loreto. Submarine geomorphology

and depositional processes along the George V

Land continental slope and upper rise (East

Antarctica). Geophysical Research Abstracts, Vol.

9, 03979, 2007, European Geosciences Union 2007.

[9] Weibel, R and Heller, M. Digital Terrain Modeling

in Maguire. J, Good child, M.F and Rhind, D.W

(eds.) Geographical Information Systems:

Principles and Applications, 1991.P.269-297,

Longman, London.

Figures:

Fig1: A schematic diagram showing the various types of accommodations used in the study along a dip profile.

http://adsabs.harvard.edu/cgi-bin/author_form?author=Dewangan,+P&fullauthor=Dewangan,%20P.&charset=UTF-8&db_key=PHY

http://adsabs.harvard.edu/cgi-bin/author_form?author=Ramprasad,+T&fullauthor=Ramprasad,%20T.&charset=UTF-8&db_key=PHY

http://adsabs.harvard.edu/cgi-bin/author_form?author=Ramana,+M&fullauthor=Ramana,%20M.%20V.&charset=UTF-8&db_key=PHY

http://adsabs.harvard.edu/cgi-bin/author_form?author=Mazumdar,+A&fullauthor=Mazumdar,%20A.&charset=UTF-8&db_key=PHY

http://adsabs.harvard.edu/cgi-bin/author_form?author=Desa,+M&fullauthor=Desa,%20M.&charset=UTF-8&db_key=PHY

http://adsabs.harvard.edu/cgi-bin/author_form?author=Badasab,+F&fullauthor=Badasab,%20F.&charset=UTF-8&db_key=PHY





Fig2: Topographic map showing present day bathymetry off the eastern offshore of India. Prominent petroliferous

basins are marked on the map. Lines marked represent the location of various profiles presented in the paper.

Fig3: Slope attribute and flow accumulation calculated on present day bathymetry data. The slope map can be used

in defining shelf, slope and basin part. Red color presents higher rate of change of slope while grey shows lowest

rate of change of slope .Flow accumulation shows the shallow as well as deep water channels forms responsible for

present day sediment dispersal pattern.

273 MRUTYUNJAYA PANIGRAHI AND MADHUMITA DAS



Fig4: Three dimensionally enclosed topographic lows are plotted on a bathymetry map of India. These represent the

area of ponded accommodation.

Fig5: Various profiles across east coast of India showing different accommodation zones identified. Sediment

deposition varies from one mini basin to another. Abbreviations used: P-Ponded accommodation, HS-healed-slope

accommodation, A-total accommodation. Positions of these profiles are presented in figure2.Vertical and horizontal

scales are in “meters”. Vertical scale is for elevation and horizontal scale shows distance.





Fig6: Various type of probable slope deposits are marked on the east coast of India based on studies of the different

attributes and the trend surface analysis made on them.

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Indexed in





ISSN 0974-5904, Volume 07, No. 01

February 2014, P.P.275-279


Geospatial assessment of Coral and Mangrove Environs of the

Andaman Islands

MAHENDRA R S1, MOHANTY P C

1, BISOYI H

2 AND SRINIVASA KUMAR T

1

1Indian National Centre for Ocean Information Services (INCOIS), Hyderabad – 500 055

2Central Institute of Fisheries Nautical & Engineering Training, Vishakhapatnam- 530001


Abstract: An archipelago island system of Andaman is consisting of several hundreds of islands. The coastal

environs of these islands are rich in bio-diversity. Most of the islands rimed with fringing corals and healthy

mangroves observed near the creeks/streams in the coastal zones. The Sumatra Earthquake occurred on December

26, 2004 recorded a 9.3 Mw not only generated devastating tsunami, but also created lot of tectonic disturbances in

the Andaman region. As result of this, northwestern parts of the land got uplifted above a meter from the earlier

position. This resulted in the lot of spatial disturbances in the coastal environment. The shallow depth corals were

exposed and degraded permanently. Moreover, the mangroves in the up streams were also degraded. A case study

from the Interview Island in the northern Andaman was selected to assess the changes. The study was carried out

using the Landsat Enhanced Thematic Mapper (ETM) and Indian Remote Sensing (IRS) Linear Imaging Self

Scanning Sensor (LISS)-III data were used to infer the spatial changes in the coral and mangrove environments. The

assessment was carried out using the Remote Sensing and GIS techniques. The results of the study reveal that the

total coral reef area of 17. 82 km2

degraded. The mangrove also showed the same tendency of degradation of total

4.48 km2 area. The techniques and the data were used in the study were given useful insight. The results help in

understanding the spatial extent and the distribution of the damage caused due to this natural calamity on the coral

and mangrove environment.

Key words: Coastal Zone, Remote Sensing, GIS, earthquake, fringing reef, mortality.

1. Introduction:

Coastal resources are crucial factor to support life of

coastal community. It is very essential that the

sustainable use of coastal resources to meet the present

and future needs. The important coastal natural

resources which are very much useful are Mangroves,

Coral Reefs, useful Seaweeds, Wetlands, Minerals,

Hydrocarbon and other organisms (Walters et al. 1998;

Jin et al. 2002). The majority of human population

(more than 60%) lives along the coastal zones and most

of communities and industries are depending on local

resources for their livelihood. Mangrove and coral

ecosystems are extremely important coastal resources.

They are coastal ecosystems providing shelter for

diverse habitats from different species and serves as a

source of food, medicines, and forestry products. In

addition to these mangrove and coral ecosystem, they

also indirectly support to economic activity through

nutrient recycling, water purification, and flood control.

Coral Reefs and mangroves buffer along the specific

coastlines act as a natural barrier for coast to protect

from storm surges, Tsunamis, cyclones, floods, sea level

rise, wave action and coastal erosion.

The mangrove and coral reef are the important natural

resources need the immense attention towards the

sustainable conservation. The remote sensing is one

such powerful tool to map these resources in order to

assess the spatio-temporal changes. Besides, the

Geographic Information System (GIS) facilitates to

extract the vital information out of the spatial datasets.

There are several such works were carried out on

mangroves (Srinivasa Kumar et al. 2011; Blasco et al.

1998; Giri et al. 2007; Kathireshan and Rajendran 2005;

Danielsen 2005) and coral reefs (Bahuguna et al. 2008;

Mahendra et al. 2008; Mahendra et al. 2010; Rajendran

et al. 2008) monitoring using the geospatial techniques.

However, the work on the exact quantification in terms

of space and time was not brought out in this area.

Hence present study aims at the demonstrating the

technology to decipher the spatio-temporal changes in

the coral and mangrove cover due to 2004 Sumatra

earthquake and tsunami in the Interview Island

Andaman. This study has brought out the accurate

changes those caused in the island due to 2004 tectonic

disturbances with the aid of the satellite data from

Landsat Enhanced Thematic Mapper (ETM) and Indian

276 Geospatial assessment of Coral and Mangrove Environs of the Andaman Islands



Remote Sensing Satellite (IRS) P6 Linear Imaging Self-

scanning Sensor (LISS) III.

2. Study Area:

The Andaman and Nicobar group of Islands are an

example of archipelagoes system which covers about

350 islands (Bahuguna et al. 2008). Interview Island lies

in the northwestern part of Andaman Islands (Figure 1)

in the Bay of Bengal. The geographical constraints of

the Interview Island are 12.76 N to 13.00N latitudes and

92.64 E to 92.73 E longitudes. The island covers a total

geographic area of about 88 km2. The habitation in the

island is nil except government and security officials.

Andaman falls under tropical climatic condition

experiencing a temperature within the range of 23°C to

31°C. There are no severe climate conditions in the

region except for tropical storms and rains in late

summers and monsoons.

Fig1: Map showing the study area

3. Data Used:

The present study was carried out based on the available

remote sensing data given in the Table 1with the aid of

the in-situ observations. The Landsat ETM data

acquired on February 07, 2000 used as the pre-tsunami

and IRS-P6 LISS-III data acquired on the February 06,

2006 was used as the post-tsunami information. The

Landsat ETM data acquired on April 10, 2010 to assess

the recent changes in the mangrove cover.

Table1: Satellite data used for the study

Satellite Sensor Date

Acquisition

Spatial

Resolution

IRS-P6

(Post

Tsunami)

LISS-

III Feb 06, 2006 23.5 m

Landsat

(Recent) ETM Apr 10, 2010 30 m

Landsat

(Pre

Tsunami)

ETM Feb 07, 2000 30 m

4. Methodology:

The spatiotemporal assessment of mangrove and coral

reefs involves three main steps viz: pre-processing;

processing and post processing. The pre-processing

consists of the geo-correction, area selection, radiance

conversion and re-sampling. The Landsat ETM Ortho-

rectified data acquired in 2000 and 2010 are

downloaded from www.landsat.org website.

Resourcesat-1 (IRS P6) LISS-III data of 2006 was

obtained from the NRSC. IRS P6 LISS-III digital data

of July 22, 2006 was geo-referenced for polynomial

order 2 using Landsat ETM data as the reference. A

subset of a mangrove and coral reef area were extracted

from all the images in order to minimize the

classification inaccuracies. The appropriate band

selection (Selvam et al. 2003; Brian and Timothy, 1996;

Green et al. 1998; Chauhan and Dwivedi, 2007;

Srinivasa Kumar et al. 2011) and the radiance

conversion techniques (Lunetta, 1999; Chauhan and

Dwivedi, 2007; Singh, 1989)

were applied on the

images before the classification. Then the LISS-III data

of spatial resolution 23.5 m has been resample to 30 m

in order to be spatially comparable with ETM. The

above two steps are making the multi-temporal and

multi-resolution satellite data comparable spectrally and

spatially respectively.

The processing of remote sensing consists of

classification and finalization of the classes using

contextual editing. Iterative Self Organizing Data

Analysis Technique (ISODATA) clustering was

performed on individual images to segment them into

possible classes each depending upon the spectral

signatures in green, red and infra-red bands. Using

contextual editing the mangrove cover pertaining to the

periods 2000, 2006 and 2010 were separated from other

classes.

Similarly another step performed in order to classify

eco-morphological classes of coral reefs during 2000

and 2006. The final classes of the coral eco-morphology

were obtained by contextual editing with the aid of the

ground truth information.

277 MAHENDRA R S, MOHANTY P C, BISOYI H AND SRINIVASA KUMAR T



In order to carryout post-processing using GIS analysis

composites of the mangrove and coral eco-morphology

composites were converted to Environmental Systems

Research Institute (ESRI) shape files by raster to vector

conversion techniques. These shape files were analyzed

using GIS package ESRI Arc Map to assess the

spatiotemporal changes in the coral and mangrove

environs.


The current study focused on the assessment of the

spatiotemporal changes in the coral reef and mangrove

of interview Island before and after 2004 Sumatra

earthquake. This earthquake has caused lot of tectonic

disturbances in the region resulted in the land up lift

(Toiba et al. 2006 and Rajendran et al. 2008) up to a

meter and half. The earthquake caused devastating

tsunami taking the life of several people and resources,

which has not left its impact on even mangrove and

corals. This impact is catastrophic on the corals and

whereas not that catastrophic when compared to coral.

However, mangroves were recorded the spatial decline

on relatively gradual time scale (Figure 2A). The

mangrove change study was continued till 2010 to get

clear scenario. The results of the spatiotemporal changes

in the mangrove reveal net spatial decline of mangrove

cover was 4.48km2 in the Interview Island (Figure 3A).

The rate of decrease in the mangrove cover was

0.3km2/y during 2000 to 2006. But, it was decreased by

0.67km2/y during 2006 to 2010 in a span of 4 years.

Fig2: The plate showing the spatio-temporal changes in

the mangrove cover (A) and coral eco-morphology (B)

The catastrophic impact on the shallow corals resulted

in the mass coral mortality due to the land uplift resulted

in the shallow corals to expose above water

permanently. The change in coral eco-morphology

(Figure 2B) was carried out using pre-earthquake (data

acquired on 2000) and post-earthquake (data acquired

on 2006). The results (Figure 3B) reveal that total 17

km2 area of corals were degraded and recorded as

exposed coral reefs in 2006. It was the part of the

healthy coral environment earlier (2000).

Fig3: The bar diagrams showing spatial changes in

mangroves during 2000-2010 (A), changes in the coral

eco-morphology classes during 2000-2006 (B)

The tectonic induced uplift in the parts of the Interview

Island caused the degradation of the coral reefs in the

area. Further corals in the area were further subjected to

the bleaching during summer months of 2010 by the

elevated temperatures (Krishnan et al. 2011; and

INCOIS, 2011) indicating the threat imposed on the

coral environs in the area. The whole Andaman

experienced the uplift in the northwestern parts and

subsidence in the southeastern parts. The coasts

experienced up-lift resulted in the degradation of

mangroves in the up-stream area at the distal ends

(landward side) of the coast due to reduction of the

saline influence. The tendency of the mangrove

community might move towards the proximal end

(seaward side) with young/new mangroves. Conversely,

278 Geospatial assessment of Coral and Mangrove Environs of the Andaman Islands



mangroves degraded at the proximal end in the land

subsided coasts due to the increase in the saline

condition might result in growth of young/new

mangroves at distal end.

6. Conclusions:

The present study aims at demonstrating the geospatial

techniques such as remote sensing and GIS to quantify

the impacts of natural disasters on the spatiotemporal

changes in the coral and mangrove environs. The data

and techniques used in the study are able to quantify

spatial changes at enhanced accuracy. Such studies are

useful in understanding the damage caused on the

important eco-systems. Further the study gives input to

the eco-system modeling to understand diversity index

and the implications on production in the coastal marine

environment.

7. Aknowledgements:

The authors would like to thank Dr. Shailesh Nayak,

Secretary, MoES for encouragement. Authors are

thankful to Director, INCOIS for facility and support.

Thanks to CARI Andaman for support during in-situ

campaign. Thanks to Global Observatory for Ecosystem

Services (GOES), Michigan State University for the

Landsat data. This is INCOIS contribution number.

8. Reference:

[1] Bahuguna, A. Nayak, S. and Roy, D. (2008) Impact

of the tsunami and earthquake of 26th December

2004 on the vital coastal ecosystems of the

Andaman and Nicobar Islands assessed using

RESOURCESAT AWiFS data, International

Journal of applied Observation and

Geoinformation, Vol. 10, pp. 229-237.

[2] Blasco F, Gauquelin T, Rasolofoharinoro M, Denis

J, Aizpuru M, Caldairou V (1998) Recent advances

in mangrove studies using remote sensing data.

Marine and Freshwater Research Vol. 49, No. 4,

pp. 287–296.

[3] Brian G. Long and Timothy D. Skewes (1996) A

Technique for Mapping Mangroves with Landsat

TM Satellite Data and Geographic Information

System. Estuar Coast Shelf Sci Vol. 43, pp. 373–

381.

[4] Chauhan HB, Dwivedi RM (2007) Inter sensor

comparison between RESOURCESAT LISS III,

LISS IV and AWiFS with reference to coastal

landuse/ landcover studies. Int J App Earth Obs

Geoinfo Vol. 10, No. 2, pp. 181-185.

[5] Danielsen, F., Sørensen, M.K., Olwig, M.F.,

Selvam, V., Parish, F., Burgess, N.D., Hiraishi, T.,

Karunagaran, V.M., Rasmussen, M.S., Hansen,

L.B., Quarto, A., Suryadiputra, N. (2005). The

Asian tsunami: a protective role for coastal

vegetation. Science Vol.310, pp. 643.

[6] Giri, C., Bruce, P., Zhiliang, Z., Ashbindu, S. and

Tieszen, L.L. (2007) Monitoring mangrove forest

dynamics of the Sundarbans in Bangladesh and

India using multi-temporal satellite data from 1973

to 2000. Estuar Coast Shelf Sci Vol. 73, pp. 91-100.

[7] Green, E.P., Clark, C.D., Mumby, P.J., Edwards,

A.J. and Ellis, A.C. (1998) Remote sensing

techniques for mangrove mapping. Int. J. Remote

Sens Vol.19, No.5, pp.935-956.

[8] INCOIS, (2011) Coral Bleaching Alert System,

WWW.http://www.incois.gov.in/Incois/coralwarnin

g.jsp, Accessed on 10 May 2011.

[9] Jin, J., Shenghong, R. and Lingjie, Z. (2002). A

study on the cost of coastal zone resources (in

Chinese with English abstract). Marine

Environment Science, Vol.21, No.1, pp.63-67

[10] Kathiresan, K. and Rajendran, N. (2005) Coastal

mangrove forests mitigated tsunami, Estuar Coast

Shelf Sci Vol.65, pp.601-606.

[11] Krishnan P, Dam Roy S, Grinson George,

Srivastava RC, Anand A, Murugesan S,

Kaliyamoorthy M, Vikas N, Soundararajan R

(2011) Elevated sea surface temperature during

May 2010 induces mass bleaching of corals in the

Andaman. Current Science, 100 (1): 111-117.

[12] Lunetta, R.S. (1999) Remote Sensing Change

Detection; Environmental Monitoring Methods and

Applications. In: R.S. Lunetta and C.D. Elvidge

(Eds), Taylor & Francis, London, pp 318.

[13] Mahendra, R. S., Bisoyi, H., Prakash, C. M.,

Velloth, S., Sinivasa Kumar T., Bahuguna, A. and

Nayak, S., 2008. Spatio-temporal Variations in the

Coral Environs of North Reef Island, Andaman: A

Remote Sensing and GIS approach, ISRS

Symposium, Ahemedabad, India.

[14] Mahendra, R. S., Bisoyi, H., Prakash, C. M.,

Velloth, S., Sinivasa Kumar T. and Nayak, S.

(2010) Applications of the Multi-spectral Satellite

data from IRS-P6 LISS-III and IRS-P4 OCM to

Decipher Submerged Coral Beds around Andaman

Islands. International Journal of Earth Sciences and

Engineering, 3(5), 626-631.

[15] Rajendran, K., Rajendran, K., Earnest, C.P., Ravi

Prasad, A., Dutta, G.V.K., Ray, D. K. and Anu, R.

(2008). Age estimates of coastal terraces in the

Andaman and Nicobar Islands and their tectonic

implications. Tectonophysics, Vol.45, pp.53–60.

[16] Selvam, V., Ravichandran, K, K., Gnanappazham,

L. and Navamuniyammal-Taramani, M. (2003)

Assessment of community-based restoration of

Pichavaram mangrove wetland using remote

sensing data. Current Science, Vol.85, No.6,

pp.795-797.

[17] Singh A (1989) Review article—digital change

detection techniques using remotely sensed data, Int

J Remote Sens., Vol.10, No.6, pp989–1003.

279 MAHENDRA R S, MOHANTY P C, BISOYI H AND SRINIVASA KUMAR T



[18] Srinivasa Kumar, T., Mahendra, R.S., Nayak, S.,

Radhakrishnan, K.R. and Sahu, K.C. (2012)

Identification of hot spots and well managed areas

of Pichavaram mangrove using Landsat TM and

Resourcesat – 1 LISS IV: An example of coastal

resource conservation along Tamil Nadu Coast,

India. Journal of Costal Conservation, 26(3), 523-

534.

[19] Tobita, M., Suito, H., Imakiire, T., Kato, M.,

Fujiwara, S. and Murakami, M. (2006) Outline of

vertical displacement of the 2004 and 2005 Sumatra

earthquakes revealed by satellite radar imagery,

Earth Planets Space, Vol.58, e1-e4.

[20] Walters, J.S., Maragos, J., Siar, S. and White, A.T.

(1998) Participatory Coastal Resource Assessment:

A Handbook for Community Workers and Coastal

Resource Managers. Coastal Resource Management

Project and Silliman University, Cebu City,

Philippines.

www.cafetinnova.org

Indexed in





ISSN 0974-5904, Volume 07, No. 01

February 2014, P.P.280-288


Integrating Fuzziness to Wildlife Relocation and Habitat Analysis in

Rajasthan, India

SUMAN SINHA Department of Remote Sensing, Birla Institute of Technology, Mesra, INDIA


Abstract: This study highlights the impact of adding uncertainty or fuzziness when selecting the most suitable sites

for tiger relocation in the Sariska Wildlife Reserve using the Analytic Hierarchy Process (AHP) within multi-criteria

based Geographic Information System (GIS). Fuzzy AHP that incorporates a Wide Trapezoidal level of uncertainty

is found to generate more variability in map outputs compared to lesser levels of uncertainty. The relative difference

between crisp (CAHP) and fuzzy (FAHP) AHP averages 2.7%, although with certain types of uncertainty it can

reach up to 5%. ANOVA also shows better results for increased levels of uncertainty compared to lesser ones. The

method adopted in the study can effectively handle the uncertainty issue, and it can act as a useful tool for wildlife

habitat evaluation and management.

Keywords: Tiger habitat, GIS, AHP, uncertainty, multi-criteria.

1. Introduction:

The wisdom of using a multi-criteria approach that is

integrated with a Geographic Information System (GIS)

in order to make important geographical value

judgements is indicated by its extremely diverse range

of real-world applications - environmental planning,

ecology management, urban planning, hydrology,

forestry, transportation, agriculture, natural hazard

management, health care resource allocation, etc.

(Vahidnia et al., 2008).

Frequently incorporated into such approach is the

Analytic Hierarchy Process (AHP), as originally

developed by Thomas Saaty (1980; 1988). This is one

of the most popular tools for estimating criterion

weights and overall scores (Taylor, 2004; Vaidya and

Kumar, 2006) and despite being often condemned for its

inability to accommodate the uncertainty and ambiguity

that surrounds practical decision taking (Deng, 1999),

not to mention its other limitations (Yang and Chen,

2004), it has been used extensively (Banai-Kashani,

1989; Eastman et al., 1992, 1993; Xiang and Whitley,

1994) and for spatial problems (Anselin et al., 1989;

Kangas, 1992; Correa-Berger, 2007; Sharma et al.,

2012a).

Nevertheless, humans lack an ability to make

quantitative predictions, although they are relatively

efficient at qualitative assessments which, in complex

situations, are usually symbolized by vague linguistic

terms. Hence practical decision makers generally make

uncertain estimations rather than specify precise values.

This is why fuzzy set theory evaluation methods have

been developed in order to refine linguistic

representations into quantitative data (Leung and Chao,

2000; Kulak and Kahraman, 2005; Özdağoğlu and

Özdağoğlu, 2007).

The result is that within to the complexity and

uncertainty of real world decision problems, fuzzy

judgments can potentially generate better and more

practical decisions than can crisp, but spuriously

accurate judgments based on traditional AHP can.

Accordingly, Mikhailov and Tsvetinov (2004) suggest a

fuzzy form of AHP (FAHP) which, in theory, will better

tolerate vagueness or ambiguity. Moreover, Vahidnia et

al., (2008) elucidated the use of FAHP in GIS and

multiple criteria decision making in spatial planning -

problems such as site selection involving the evaluation

of a set of alternatives on the basis of conflicting and

incommensurate criteria (Malczewski, 1999). Put

differently, because decision makers usually feel more

confident when giving interval judgments rather than

single numeric values, conventional AHP is unable to

reflect the human thinking style completely. By

contrast, FAHP can capture human perception of

ambiguity in complex, multi-attribute decision making

problems (Erensal et al., 2006).

Fuzzy numbers, introduced by Zadeh (1965; 1973)

allow membership functions to operate over the range of

real numbers [0, 1], and the primary feature of fuzziness

is the assembling of individuals into classes where

sharply defined boundaries are absent (Hansen, 2005).

Hence, fuzzy numbers can represent and define

uncertain judgments, whereas AHP does not take in


281 SUMAN SINHA



consideration the uncertainty associated with the

judgements of the decision makers’ (Yang and Chen,

2004).

It is hardly surprising, therefore, how Saaty’s AHP

process has been modified and fuzzified to formulate

and control uncertainty and ambiguities. Specifically,

researchers such as Buckley (1985) devised an

innovative approach of the trapezoidal membership

function in AHP, and Chang (1996) formulated a

modified case of a triangular membership function

which makes it easier for decision makers to understand

the final importance, and the underlying uncertainty of

parameters with defined uncertainty in the form of fuzzy

numbers from which weights can be determined. Saaty

and Tran (2007) have countered all this by pointing out

that uncertainty already persists in AHP and this ensures

that the ratios in the method are not absolute or crisp

numbers but are, in fact, fuzzy numbers, and so

fuzzifying AHP does not ensure better results. In fact, it

could even make the analysis worse. Unfortunately,

however, most comparisons between Crisp AHP

(CAHP) and Fuzzy AHP (FAHP) have been mostly

done in this sort of theoretical way, with very limited

practical applications of the methods to a genuine, GIS-

based decision making problem.

Hence the goal of this paper is to practically evaluate

the differences between CAHP and FAHP methods by

applying them to one particular real-world problem that

is plagued by uncertainty within its parameters - tiger

relocation and habitat suitability in the Sariska Wildlife

Reserve (SWR), Rajasthan, India. Here uncertainty is

due to the dynamism of landscapes resulting from

changes in nature, human activities and socioeconomic

conditions (Sharma et al., 2012b).

2. Methodology:

2.1. Theory background:

AHP uses a Pairwise Comparison Method (PCM) to

obtain weights for the evaluation criteria (Boroushaki

and Malczewski, 2008), and this paper considers two

different approaches to it - the Lambda Max (λmax)

technique (Saaty, 1980) and the geometric mean method

(Buckley, 1985). Every comparison matrix has a set of

eigenvalues and for every eigenvalue there is a

corresponding eigenvector, and in Saaty’s lambda max

technique, a vector of weights is defined as the

normalized eigenvector corresponding to the largest

eigenvalue, λmax. The latter is then used for calculating

the consistency ratio (C.R.) and Consistency Index

(C.I.). If C.R. < 0.10 or C.R. ≥ 0.10 a reasonable level

of consistency in the pairwise comparisons is indicated

(Han and Tsay, 1998; Malczewski, 1999).

A fuzzy set comprises of four parameters of the fuzzy

number α, β, γ and δ where 0<α≤β≤γ≤δ. That is, a

range of values is assigned instead of a single numeric

value because the decision-maker feels insecure if they

fix a definite value to a particular factor. Fuzzification

of AHP by the geometric mean method is accepted and

applied easily (Buckley, 1985; Sinha et al., 2011a), and

it uses a triangular membership function, β ij = γ ij. To

increase the level of uncertainty, the distribution of the

fuzzy number is extended at β ij ≠ γ ij.

Hence, the difference between CAHP and different

uncertainty levels of FAHP can be analyzed, and here,

γij−βij with a value of 1 and 2 generated FAHP narrow

and wide trapezoidal ratios respectively. The resulting

fuzzy number (wi) has to be defuzzified in order to

obtain a singular crisp value (Sinha et al., 2011a), here

we have used a simple centroid method that uses the

centre of gravity concept (Opricovic and Tzeng, 2003;

Chang and Wang, 2009) to do so.

Finally, there is an urgent need for improved

conservation strategies to preserve endangered wild

tigers - given that they are globally declining and being

eradicated. In fact, during 2005 the Sariska Wildlife

Reserve (SWR) in India was devoid of tigers due to

extensive poaching (Wildlife Institute of India Report,

2008; Project Tiger Report, 2009). So this study aims at

a geospatial solution using comparative analysis

between CAHP and FAHP when they are applied to

GIS-MCDA methods for finding the most suitable sites

for tiger habitat and future relocation.

2.2. Study area and dataset:

SWR is located among the Aravalli hill ranges in the

Alwar district of Rajasthan, India and it covers an area

of nearly 1183 km2, which is taken as the study area. It

extends from 27º13' to 27º31'N latitude and from 76º15'

to 76 º 33’ E longitudes (Figure 1). IRS P-6 LISS III

satellite data for 2006, which have a spatial resolution of

23.5 m, were used for the study, along with other

ancillary and secondary data including the Survey of

India toposheets. The reason for choosing this particular

area is that tigers were completely exterminated there

during 2004-2005 despite the locality being highly

suitable for their habitats. A few tigers have been

relocated since, and identification of the suitable areas

for the tigers is the main challenge.

282 Integrating Fuzziness to Wildlife Relocation and Habitat Analysis in Rajasthan, India



Fig1: Location of study area. SWR is located in the state of Rajasthan (yellow), India.

2.3. Evaluating criteria in integrated GIS-MCDA:

The impact of human intrusion upon habitat

suitability is substantial for all faunal species in

general and for the tigers of Sariska in particular

(Sinha et al., 2011b; 2012). Accordingly, map

overlay techniques incorporating rules and criteria

were used for habitat evaluation in the Chilla

Sanctuary of Rajaji Wildlife Reserve, India

(Kushwaha et al., 2000). In order to reflect habitat

suitability given the preferences of the species under

consideration, appropriate weights were assigned to

factors, and for tigers such factors were generated

from an extensive literature survey, reconnaissance

information, expert knowledge and field

investigations.

The most significant habitat-selection parameters

used in this study were:

1. land use / land cover (forest type and density,

waterbodies, settlements, road),

2. terrain slope and

3. anthropogenic activities.

Each parameter was represented as a thematic layer

in GIS, from which an output map was derived to

show five suitability categories (priority classes)

highly suitable (class 5),

very suitable (class 4),

moderately suitable (class 3),

poorly suitable (class 2) and

least suitable or unsuitable (class 1).

Fig2: Methodology for deriving tiger habitat-suitability maps, using GIS-MCDA and AHP.

SWR

INDIA

Rajasthan

283 SUMAN SINHA



The study’s methodology is shown in Figure 2. It

took various input parameters determined from both

remote sensing and ancillary data before

amalgamating them with information from field

surveys. The field surveys were carried out to gather

information related to tiger habitat use, forest type and

vegetation density. The satellite image was initially

classified using the supervised classification technique

within the Maximum Likelihood operation found in the

ERDAS Imagine (version 9.1) software. Also, the

thematic maps showing forest type, forest density,

roads, settlements, water bodies and slopes were

generated in the ArcGIS (version 9.3) software while

taking into account both visual and digital (spectral

indices) interpretations.

Note that prey availability was not regarded as a

significant factor (Sinha et al., 2011a) because in

Sariska, prey is available in plenty - as revealed by high

levels of prey density. Moreover, the area is large and

rich enough to support other predators without much

struggle and competition. Note also that every criterion

represented as a map in the GIS database can be

combined with MCDA (Malczewski, 1999; Baban and

Wan-Yusof, 2003). Knowledge-based AHP was used as

the primary method for assigning appropriate weights to

the parameters, and the result was the matrix shown in

Table 1. Comparison values were based on personal

experience, expert advice and an understanding of the

decision making problem. The multi-criteria approach

was then integrated with different uncertainty levels of

AHP (Triangular, Narrow Trapezoidal and Wide

Trapezoidal).

Table1:Pair-wise comparison matrix for parameters selected.

PARAMETERS Vegn. type Vegn. density Settlement Water hole Drainage Road Slope

Vegn. type 1 3 3 3 3 5 7

Vegn. density 1/3 1 1/3 1/3 1/3 3 5

Settlement 1/3 3 1 1/3 1/3 3 5

Water hole 1/3 3 3 1 1 3 7

Drainage 1/3 3 3 1 1 3 7

Road 1/5 1/3 1/3 1/3 1/3 1 3

Slope 1/7 1/5 1/5 1/7 1/7 1/3 1

2.4. Fuzziness:

The following equations were used for fuzzifying

AHP and calculating weights (Buckley, 1985; Sinha

et al., 2011a):

n

1

/1n

1j

n

1

/1n

1j

n

1

/1n

1j

n

1

/1n

1j

,]ij[

,]ij[

,]ij[

,]ij[ i

i

n

i

n

i

n

i

n

ii

ii

ii

i

wi=[( i/ ), (

i/

), (

i /

), ( I / )]... (Eq.1)

Pairwise comparison matrices with different degrees

of uncertainty, as obtained from the CAHP and the

three FAHP methods, were considered. Chi-Square

was then used to statistically check the effect of

fuzzification on the resulting maps in terms of the

degree of uncertainty. Weights for each factor were

calculated by the AHP Lambda max (λmax) method

and then normalized and defuzzified using the

FAHP Triangular, FAHP Narrow Trapezoidal and

FAHP Wide Trapezoidal methods respectively, as

documented in Table 2.

3. Results:

3.1. Comparison of uncertainty levels:

A weighted overlay model in GIS-MCDA was used to

produce a suitability map for tiger habitat with different

uncertainty levels. The model considered both the

weights and relative importance factors, as required in

index modeling, in order to calculate the pixel number

for each suitability or priority class, as shown in Table

3, and these were then subjected to a Chi-Square test. In

Chi-Square testing we used the null hypothesis (H0) that

the level of uncertainty does not affect the difference

between the fuzzified map and the non-fuzzified map,

and the standard formula was used (Dowdy et al., 2004).

χ2 =

iii

i E

E- O2

….. (Eq.2)

Where, O and E are the observed and expected number

of pixels respectively.




Table2: Weights assigned to factors by the knowledge-based and different AHP methods.

Parameters Knowledge-

based

AHP

λmax

AHP

Geometric

Mean

FAHP

Triangular

FAHP Narrow

Trapezoidal

FAHP Wide

Trapezoidal

Vegn. type 0.25 0.331555 0.334036 0.32725 0.32798 0.31507

Vegn. density 0.10 0.09202 0.084322 0.08696 0.08682 0.09076

Settlement 0.15 0.121896 0.115415 0.11763 0.11755 0.12052

Waterholes 0.15 0.18736 0.193919 0.20067 0.20414 0.20159

Drainage 0.10 0.18736 0.193919 0.18733 0.18343 0.18947

Road 0.15 0.054118 0.05324 0.05513 0.05502 0.05789

Slope 0.10 0.02569 0.025149 0.02501 0.02504 0.02468

Note that the suitability map from AHP showed no

pixels in class 1, so it was omitted from the chi-square

test because the value ‘0’ should be kept out of the

analysis. Table 4 provides the results from the Chi-

square analysis of different uncertainty levels of the

fuzzy AHP and the CAHP Lambda max method. It

indicates that two of the calculated values are smaller

than the critical value of 7.815 at α = 0.05 and DF = 3

used in the study - fuzzy AHP triangular and fuzzy AHP

narrow trapezoidal. In these cases, the observed and

expected values are similar and hence, the Null

Hypothesis is accepted. By contrast, the value is

exceedingly higher than the critical value in fuzzy AHP

Wide Trapezoidal, implying the rejection of the Null

Hypothesis, simultaneously accepting the Alternative

Hypothesis. This shows a prominent relationship of the

degree of uncertainty with the difference in the maps of

spatial extent of every priority classes.

Table3: Number of pixels in each suitability class as derived from map outputs

Method No. of pixels in each suitability class

Class 1 Class 2 Class 3 Class 4 Class 5 Total

CAHP λmax 0 12457 31758 24783 1974 70972

FAHP Triangular 0 12326 32018 24622 2006 70972

FAHP Narrow Trapezoidal 0 12537 31943 24580 1912 70972

FAHP Wide Trapezoidal 0 11065 33244 24847 1816 70972

Such results show that the methods applied have

significant impacts upon the outputs. The analysis

shows greater extent of similarity between CAHP and

FAHP Narrow Trapezoidal method. On the other hand,

the FAHP Triangular method is more similar to FAHP

Narrow Trapezoidal method than compared to FAHP

Wide Trapezoidal method. This reveals possible

differences in the resulting maps arising due to the

difference in the uncertainty levels in AHP methods.

3.2. Differences in fuzzy uncertainty:

Differences between the weights obtained from each of

the fuzzified and non-fuzzy methods were calculated in

percentage terms using Eq. 3

Difference =

100wi

wi'-wi ….. (Eq.3)

Where wi' and wi are respectively the defuzzified weight

number i calculated through fuzzy AHP and the weight

number i calculated using the non-fuzzy AHP λmax

method. Results show slightly more than 5% deviation

in the uncertainty levels.

Table4: Results of Chi-Square test

Method FAHP

Triangular

FAHP

Narrow

Trapezoidal

FAHP

Wide

Trapezoidal

χ2

value 5.070879 5.201557 237.8918

Table 5 shows the relative differences in the weights

assigned by all the methods expressed in percentages

and it reveals a marked difference among the different

fuzzy methods. Any increase in the levels of uncertainty

gives rise to greater difference in the map outputs and

the differences are prominent. These results were further

confirmed using ANOVA which generated significance

of 0.118 at the significance level of 0.05, for the FAHP

Wide Trapezoidal method in comparison to the

remaining methods adopted in the study. By contrast,

CAHP, FAHP Triangular and FAHP Narrow

Trapezoidal methods resulted in the values of 0.164,

285 SUMAN SINHA



0.139 and 0.136 respectively. The analysis also showed

the relative differences among the different uncertainty

levels. Hence the statistical differences among the

uncertainty levels, in the context of spatial dimensions

(pixel numbers in map outputs) are proved from the

above methods.

Table5: Relative difference (percent) among different AHP methods from map outputs

FAHP Triangular FAHP Narrow Trapezoidal FAHP Wide

Trapezoidal

CAHP 1.4% 1.3% 2.7%

FAHP Triangular 1.4% 2.3%

FAHP Narrow Trapezoidal 2.6%

Fig3: Tiger habitat suitability maps derived from fuzzy AHP Wide Trapezoidal (left), crisp AHP (middle) and

knowledge-based estimates (right) methods with differences in the output encircled.

3.3. Habitat evaluation:

The weights obtained from AHP methods were used to

model the tiger habitat suitability via weighted sum

cartographic index modeling. The habitat model (HM)

for tigers is shown in Eq. 4.




n

i

HM1

fi) x (wi

….. (Eq.4)

where, f is the factor considered in the habitat suitability

model, w is the respective weight assigned to each

factor through AHP methods as mentioned in Table 2,

and n is the number of factors considered - i = 1, 2 …n.

Figure 3 shows the output map that represents the best

probable result, as obtained from FAHP Wide

Trapezoidal method in MCDA for site suitability of

tiger habitats. The figure also highlights the differences

in area obtained as a result of the fuzzy and non-fuzzy

methods, thereby showing the effect of fuzzification in

GIS-based real world conditions.

Extensive ground truth in conjunction with expert

opinion was undertaken for the authentication of these

results. The map shows highly suitable areas marked in

red, very suitable in light blue and moderately suitable

in dark blue.

4. Discussion and conclusions:

Our study provides an insight of the effectivness of

spatial fuzzy modeling for habitat suitability to restore a

highly endangered population in the locality through

geospatial MCDA techniques. We have shown that

geospatial data integrated with expert knowledge and

implemented within a criteria-based GIS approach can

be used for site suitability studies for wildlife habitat

evaluation and conservation with regard to the

environmental conditions suitable for a species. The

importance of the factors accounting for the analysis can

be determined by the weights generated through AHP

integrated with GIS and overlay analysis.

We concluded that Sariska is an appropriate habitat for

tigers and hence a suitable place for their relocation.

For tiger habitat suitability in this case, and other real-

world situations as well, the weights assigned by

increased uncertainty, in terms of fuzzy AHP methods,

are found to be extremely effective wherever a number

of variables are considered together. The resultant

outputs in the form of maps showed the effects of

uncertainty levels in AHP. A prominent correlation

exists between the degree of uncertainty and spatial

difference between the maps in GIS, as also revealed by

Chi-square test. From the above, a rule of thumb can be

suggested - the greater the uncertainty level the greater

is the difference. In other words, deviation is more

prominent for the FAHP that uses the Wide Trapezoidal

method. With an increase in the difference between β ij

and γ ij, the overall fuzziness increases.

Our study reveals marked variation both between the

CAHP and FAHP methods in general, and between the

different uncertainty levels in FAHP. When weights are

quantified via AHP methods, a deviation of slightly

more than 5% is noticed over the uncertainty levels. The

deviation of somewhat more than 5% is observed over

the entire uncertainty levels using the AHP methods

adopted in the study. Also, there appear to be relative

differences between CAHP and FAHP method which is

documented to approximately 2.7%.

In summary, variations in uncertainty levels result in

different outputs that are visible in the spatial

dimension. The efficacy of the methods will vary

depending on the nature and extent of the targeted,

spatial, real-world decision making problem; otherwise

this might end up giving incorrect and unjustified

results. Decision makers prefer greater degrees of

uncertainty in real world applications, and we have

shown that the results vary depending upon the degree

of uncertainty level used. As already mentioned that

AHP has inherent fuzziness and so it is also observed in

Table 2 that the weights obtained from different crisp

AHP methods (λmax and Geometric Mean) are different

revealing differences between the methods. However,

FAHP has greater utility in handling complex multi-

attribute decision making problems as interval

judgments are preferable to single numeric values in

real-world scenarios.

In complex situations, many decisions are made in an

environment where decision making process is

uncertain or indecisive and so fuzzy numbers should be

used in those cases for evaluation. That is, in spite of its

complex calculations, fuzzy AHP should be used in

such conditions. Fuzzy logic can tackle the uncertainty

and imprecision involved in expert knowledge

effectively. In our opinion, therefore, it would be safer

and more realistic if a range of values were assigned by

FAHP for a particular factor in real-world

circumstances. We cannot be very confident when

assigning a definite numeric value for any factor as they

might be influenced by a number of other variables.

Our study also supports the wisdom of applying greater

fuzziness or uncertainty in factual realistic situations, as

confirmed by the ANOVA used in our study.

As for the problem considered, tigers are regarded as an

endangered umbrella species. Exceptional care has to be

taken for their conservation as they are now confined to

selected scattered dispersed pockets of Asia, mainly in

India. Although global warming is a severe menace to

biodiversity, the MCDA crisp and fuzzy AHP methods

implemented in this study are cause for optimism

regarding their suitable site selection and relocation,

thus encouraging their conservation and proper

management. Integrating this study with several related

studies like biomass (Sharma et al., 2013; Kumar et al.,

2013) and forest fires (Sharma et. al 2012a; Kanga et

al., 2011) can add robustness in the analysis.

287 SUMAN SINHA



As for all real-world scenarios, our study also involves

parameters that are uncertain to some extent. Hence,

the authors suggest the fuzzification of classical AHP

for assigning weights in order to tackle the problem of

vagueness. The study of suitable habitat site selection

for tiger relocation shows satisfactorily acceptable

results using AHP, with inherent fuzziness, when

compared to the knowledge-based approach. Therefore,

it would be sensible for all real-world, multivariate

complex problems to be addressed with fuzzy concept

techniques.

Our study would be of significant use for management

and policy making strategies, such as forest

management, land use planning and sustainable

management of natural resources. It can help the

decision makers to decide upon the target problems as

well as an appropriate method to solve it. If the problem

does not deal with factors that are not uncertain and

vague and numerical judgments can be made, then crisp

AHP can be applied; otherwise, it is advisable to take

fuzzy interval judgments, with expert decision makers

then determining the perfect fuzzy range for the

assigned problem. Habitat protection is essential for

wildlife conservation, yet, a successful tiger recovery

and reintroduction program is also crucial along with

protecting numbers. This is extremely vital for

conserving the last traces of endangered tiger

populations as well as all supporting species and

ecosystem functions.


The author expresses sincere gratitude to the

Department of Science and Technology (DST),

Government of India for providing funds under DST-

INSPIRE Program (AORC scheme). The author is

thankful to Birla Institute of Technology, Mesra, India

where the work was done. Officials of Project Tiger,

Sariska and Sariska Forest Division (Rajasthan, India)

are acknowledged for their support.

6. Reference:

[1] Anselin, A., Meire, P.M., Anselin, L., 1989.

Multicriteria techniques in ecological evaluation: an

example using the analytical hierarchy process.

Biological Conservation, 49, 215–229.

[2] Baban, S.J., wan-Yusof, K., 2003. Modelling

optimum sites for locating reservoirs in tropical

environments. Water Resources Management, 17,

1–17.

[3] Banai-Kashani, R., 1989. A new method for site

suitability analysis: The analytic hierarchy process.

Environmental Management, 13, 685-693.

[4] Boroushaki, S., Malczewski, J., 2008.

Implementing an extension of the analytical

hierarchy process using ordered weighted averaging

operators with fuzzy quantifiers in ArcGIS.

Computers and Geosciences, 34, 399–410.

[5] Bozdag˘, C.E., Kahraman, C., Ruan, D., 2003.

Fuzzy group decision making for selection among

computer integrated manufacturing systems.

Computers in Industry, 51, 13–29.

[6] Buckley, J.J., 1985. Fuzzy Hierarchical Analysis.

Fuzzy Sets and Systems, 17, 233-247.

[7] Chang, D.Y., 1996. Application of the extent

analysis method on fuzzy AHP. European Journal

of Operational Research, 95, 649-655.

[8] Chang, T.-H.,Wang, T.-C., 2009. Using the fuzzy

multi-criteria decision making approach for

measuring the possibility of successful knowledge

management. Information Sciences, 179, 355–370.

[9] Correa-Berger, B.P., 2007. Developing a habitat

suitability model for the Spotted Turtle using a

Hybrid-Deductive approach. Thesis, Rochester

Institute of Technology, New York.

[10] Deng, H., 1999. Multicriteria analysis with fuzzy

pairwise comparisons. International Journal of

Approximate Reasoning, 21, 215–231.

[11] Dowdy, S.H., Wearden, S., Chilko, D., 2004.

Statistics for research, John Wiley and Sons Inc.,

New Jersey.

[12] Eastman, J.R., Jin, W., Kyem, P., Toledano, J.,

1992. Participatory procedures for multi-criteria

evaluations in GIS, GIS’92, Buffalo, N.Y, 281–

288.

[13] Eastman, J.R., Kyem, P., Toledano J., 1993. A

procedure for multi objective decision making in

GIS under conditions of multiple objectives,

EGIS’93, Genoa, EGIS Foundation, 438–447.

[14] Erensal, Y.C., Oncan, T., Demircan, M.L., 2006.

Determining key capabilities in technology

management using fuzzy analytic hierarchy

process: A case study of Turkey. Information

Sciences, 176, 2755–2770.

[15] Han, W.J., Tsay, W.D., 1998. Formulation of

Quality Strategy Using Analytic Hierarchy Process.

27th

Annual Meeting of the Western Decision

Science Institute, University of Northern Colorado,

USA, 580–583.

[16] Hansen, H.S., 2005. GIS-based Multi-Criteria

Analysis of Wind Farm Development,

ScanGIS'2005, H. Hauska, and H. Tveite (Ed.),

Proceedings of the 10th Scandinavian Research

Conference on Geographical Information Science,

Stockholm, Sweden: Royal Institute of Technology,

75-87.

[17] Jhala, Y.V., Gopal, R., Qureshi, Q., 2008. Status of

the tigers, co predators, and prey in India, National

Tiger Conservation Authority, Govt. of India, New

Delhi, and Wildlife Institute of India, Dehradun.

TR 08/001, 151.




[18] Kahraman, C., Cebeci, U., Ruan, D., 2004. Multi-

attribute comparison of catering service companies

using fuzzy AHP: the case of Turkey. International

Journal of Production Economics, 87, 171–184.

[19] Kanga, S., Sharma, L.K., Pandey, P.C., Nathawat,

M.S., Sinha, S., 2011. Forest fire susceptibility

mapping using GIS modeling. International Journal

of Earth Science and Engineering, 4(3): 80-87.

[20] Kangas, J., 1992. Multiple-use planning of forest

resources by using the Analytic Hierarchy Process.

Scandinavian Journal of Forestry Research, 7, 259–

268.

[21] Kulak, O., Kahraman, C., 2005. Fuzzy multi-

criterion selection among transportation companies

using axiomatic design and analytic hierarchy

process. Information Sciences, 170, 191-210.

[22] Kumar, P., Sharma, L.K., Pandey, P.C., Sinha, S.,

Nathawat, M.S., 2013. Geospatial strategy for

tropical forest-wildlife reserve biomass estimation.

IEEE Journal of Selected Topics in Applied Earth

Observations and Remote Sensing, 6(2): 917-923.

[23] Kushwaha, S.P.S, Munkhtuya, S., Roy, P.S., 2000.

Mountain goat habitat evaluation in Rajaji National

Park using remote sensing and GIS. Journal of the

Indian Society of Remote Sensing, 28, 293–303.

[24] Leung, L.C., Chao, D., 2000. On consistency and

ranking of alternatives in fuzzy AHP. European

Journal of Operational Research, 124, 102-113.

[25] Malczewski, J., 1999. GIS and multicriteria

desision analysis. John Willey and Sons Inc., New

York.

[26] Mikhailov, L., Tsvetinov, P., 2004. Evaluation of

services using a fuzzy analytic hierarchy process.

Applied Soft Computing, 5, 23–33.

[27] Opricovic, S., Tzeng, G.-H., 2003. Defuzzification

within a multicriteria decision model. International

Journal of Uncertainty, Fuzziness and Knowlege-

Based Systems, 11(5), 635-652.

[28] Özdağoğlu, A., Özdağoğlu, G., 2007. Comparison

of AHP and fuzzy AHP for the multicriteria

decision making processes with linguistic

evaluation. İstanbul Ticaret Üniversitesi Fen

Bilimleri Dergisi, 6, 65-85.

[29] Project Tiger Report- National Tiger Conservation

Authority, Government of India. 2009. Stripes,

1(1), 20.

[30] Saaty, T.L., Tran, L.T., 2007. On the invalidity of

fuzzifying numerical judgments in the Analytic

Hierarchy Process. Mathematical and Computer

Modelling, 46, 962–975.

[31] Saaty, L.T., 1988. Multicriteria decision making:

The analytic hierarchy process, RWS Publications,

Pittsburgh, PA.

[32] Saaty, L.T., 1980. The analytic hierarchy process,

McGraw-Hill International, New York.

[33] Sharma, L.K., Kanga, S., Nathawat, M.S., Sinha,

S., Pandey, P.C., 2012a. Fuzzy AHP for forest fire

risk modeling. Disaster Prevention and

Management, 21(2), 160 – 171.

[34] Sharma, L. K., Pandey, P. C., Nathawat, M.S.,

2012b. Assessment of land consumption rate with

urban dynamics change using geospatial

techniques. Journal of Land Use Science, 7(2), 135-

148.

[35] Sharma, L.K., Nathawat, M.S., Sinha, S., 2013.

Top-down and bottom-up inventory approach for

above ground forest biomass and carbon

monitoring in REDD framework using multi-

resolution satellite data. Environmental Monitoring

& Assessment, doi: 10.1007/s10661-013-3199-y.

[36] Sinha, S., Sharma, L.K., Nathawat, M.S., 2012.

Tigers losing grounds: Impact of anthropogenic

occupancy on tiger habitat suitability using

integrated geospatial-fuzzy techniques. The

Ecoscan, 1, 259-263.

[37] Sinha, S., Sharma, L.K., Nathawat, M.S. 2011a.

Retrieving tiger habitats: Conserving wildlife

geospatially. Applied Remote Sensing Journal,

2(1), 1-5.

[38] Sinha, S., Sharma, L.K., Pandey, P.C., Nathawat,

M.S. and Kanga, S., 2011b. Impact of human

intrusion on tiger habitat and conservation using

integrated geospatial techniques. International

Journal of Earth Science and Engineering, 4(3), 39-

45.

[39] Taylor, B.W., 2004. Introduction to Management

Science. Pearson Education Inc., New Jersey.

[40] Vahidnia, M.H., Alesheikhb, A., Alimohammadic,

A., Bassirid, A., 2008. Fuzzy Analytical Hierarchy

Process in GIS application. The International

Archives of the Photogrammetry, Remote Sensing

and Spatial Information Sciences, 37, 593-596.

[41] Vaidya, O.S., Kumar, S., 2006. Analytic hierarchy

process: An overview of applications. European

Journal of Operational Research, 169, 1–29.

[42] Xiang, W., Whitley, D.L., 1994. Weighting land

suitability factors by the PLUS method.

Environment and Planning B: Planning and Design,

21, 273–304.

[43] Yang, Ch. Ch., Chen, B.Sh., 2004. Key quality

performance evaluation using Fuzzy AHP. Journal

of the Chinese Institute of Industrial Engineers,

21(6), 543-550.

[44] Zadeh, L. A., 1973. The concept of linguistic

variable and application to approximate reasoning,

ERL-M411, California, Berkeley.

[45] Zadeh, L. A., 1965. Fuzzy sets. Information and

Control, 8(3), 338-353.

www.cafetinnova.org

Indexed in





ISSN 0974-5904, Volume 07, No. 01

February 2014, P.P.289-296


Benthic Foraminifera in a Sedimentary Core from Kollam Coastal

Plain, South Kerala, India

R GAYATHRI1, R NAGENDRA

1, A N REDDY

2, P SATHIYAMOORTHY

1 AND N SURESH

1

1Department of Geology, Anna University, Chennai – 600 0025, India

2201, Majestic shore, 208, Choolaimedu High Road, Chennai-600094, India

Email: [email protected].

Abstract: A sediment core of 38m length collected from the Kollam coastal plain was analyzed for benthic

foraminifera to understand paaleo environmental evolution. Sedimentological analysis of the core recognized a

sharp break in lithology at 13m depth which marked rapid upward decrease in foraminifera abundance and diversity.

Total foraminifera abundance and species diversity, however, show a linear positive relationship with increased clay

content and differing trend with increased sand content. The core analysis further revealed gradual upward transition

in faunal composition and frequency variation of the most dominant taxa A.beccarii and N.scaphum suggests

gradual environmental transition from estuarine complex to coastal plain complex in upcore direction.

Keywords: Kollam coastal plain, Benthic foraminifera, Kerala coast.

1. Introduction:

Kollam, South West part of Kerala (8◦52'48.76"N:

76°36'00.14"E), has a coastline length of 41 km (Fig.I).

It has geographical area of 2491 sq km. (Mini Chandran,

1998). Thickest Quaternary sediment. Sequence is

exposed in the South Kerala Sedimentary basin which

extends from Kollam to Kodungallur in a form of

curvilinear area with a maximum width of approx. 25

km and a thickness of approx. 80 m. (Nair and Padmalal

2004). The basin is divided into central depression

flanked by Southern Block and Northern Block. The

first marine transgression took place around 42, 000

years before present (42KyBP). The Holocene marine

transgression was experienced by about 7kyBp. This

was followed by a regression which left the present

landscape of lagoons, wetlands and the ridge-runnel

topography. Limaye et a1 (2009) studied Late

Quaternary sediments from the boreholes of PanavalIy

and Ayiramthengu of Kollam district and reported the

occurrence of cynobacteria. The present study focuses

on sedimentological and foraminifera analysis to

understand the environmental evolution through the

deposition of sediment core.

2. Methodology:

A land rig commonly used for ground water exploration

was employed to drill a sediment core of a length of

38m and recovered 100%. The lithological study of

sediment core reveals that it composed of 3m sand, 32

m lateritic clay. 13m sand with lateritic soil (0-13m),

10m clayey silt (13-23m) and 15m silty clay (23-38m).

About 10 gm dry sediment sample soaked in water for a

while and washed over a 63µm sieve with distilled

water and dried in an oven at 60°C. Then foraminifera

were picked using a stereomicroscope (NOVEX AR

200M) and mounted on faunal slides and total faunal

tests were counted. Subsequently the temporal

distribution, absolute and relative abundances of benthic

foraminifera is summarized in Table 2. Q-mode cluster

analysis was carried out on the relative abundance of 29

foraminifera. The foraminifera were taxonomically

identified by referring to Loeblich and Tappan (1988),

Murray (1971), Boltovskoy et al. (1980) and World

modern foraminifer’s database.

3. Results:

3.1 Sediments:

The table I clearly indicates a sharp change in lithology

at 13m depth, wherein sand/silt/clay ratios exhibit a

turnaround in its percentage occurrence. The core can be

divided into 3 litho units based on ratios of sand, silt and

clay. The lower litho unit (L-1) between 38-23m

contains predominance of clay (63.1-72%). The silt and

sand represents 27.1-32.8% and 0.24-0.8% respectively.

The litho unit 2 between 23m and 13m consists of clay

(55.3-64.3%), silt (25.1-32.9) and sand (2.97-12.65%).

This litho unit is transitional between L-1 and L-3,

wherein increasing trend of % silt and sand is observed.

The upper litho unit (L-3) shows predominance of sand

(lateritic) (63.9-99.99%), silt (0-20.5%) and clay (0-

18.9%).

3.2Foraminifera:


290 Benthic Foraminifera in a Sedimentary Core from Kollam Coastal Plain,

South Kerala, India



The table II shows the vertical distribution of 30 benthic

foraminifera species recorded from the sediment core at

Kollam coastal plain. It is evident from the data that

Ammonia beccarii (36.5-82.5%) is the most dominant

almost at all the study depths followed by Nonion

scaphum (7.4-54.2%). The remaining 28 species occur

sporadically. Elphidium crispum contains 11% of total

relative abundance at 3m depth, while Bolivina

spathulata contains 25% and N.boueanum 27% relative

abundance at 35m and 7m depths respectively. A.

beccarii and N. scaphum occur throughout the core

while other species show erratic presence and sparse

abundance. The figure II illustrates the hierarchical

dendrogram which explains biotopes recognized using

Q-mode cluster analysis. The single linkage distance

method recognized 8 biotopes at Eucledean distance of

52. The biotope-1 which covers the core depth mainly

between 16m and 23m, shows very close similarity in

faunal composition at a linkage distance of 10. This

interval falls in litho unit 2 (L-2) and consists of mainly

clayey silt lithology with minor sand content (2.97-

12.65%). The increased relative abundance of

N.scaphum (31.24%) and reduced abundance of

A.beccarii (56.1%) with respect to bottom L-1 unit

characterizes this biotope. Biotopes 2,5,7 and 8

envelops the upper part of the core (14-0 m depth).

However these biotopes grouped separately because of

the contribution by other taxa in addition to two

dominant taxa A.beccarii and N.scaphum. Biotope-2 has

a linkage distance of 30 and characterized by Elphidium

crispum (11%), Fursenkoina texturata (5.2%), Lobatula

lobatula (3.4%) and Nonion boueanum (2.7%). Biotope-

5 is distinct by the presence of E. crispum (1.4%), F.

texturata (1.1%) and E. hispidulum (0.4%), whereas

Biotope-7 is distinguished by N.boueanum (25%) and

Biotope-8 is diagnostic by the presence of E.crispum

(5.7%), Bolivina spathulata (4.5%), F.texturata (1.1%)

and Rectobolivina ?virgula (0.7%). Well preserved

foraminifera specis are demonstrated in Plate I.

4. Discussion:

The table I & figure III shows the vertical distribution of

total foraminifer abundance (TNF) and species diversity

(S). It is observed that a gradual increase of total

abundance and diversity in litho unit 1 from 38m to

23m,where clay (63.1-72%) is the dominant lithology

while sand represents <1%. Total faunal abundance and

diversity show a gradual increase in upcore in this litho

unit. The litho unit 2 wherein presence of silt varies

from 25.1-32.9% supports minimum abundance and

diversity of total foraminifera. The lithounit 3 contains

predominantly sand (lateritic). Faunal abundance and

diversity show a decreasing upward trend in this unit.

The turnaround in litho content, TNF and S is clearly

evident at 13m core depth, where total abundance trends

rapidly decrease upwards. A.beccarii is the most tolerant

species and occurs predominantly in all the coastal

water bodies either in polluted or natural environments

revealing its high tolerance and adaptability to changing

environmental variables. Ammonia beccarii is

ubiquitous and dominant in all the coastal water bodies

like lagoons and estuaries along the TamilNadu and

Kerala coasts (Ramnathan, 1970; Reddy and Reddy,

1982; Jayaraju and Reddy, 1996; Kumar et al. 1996;

Gandhi et al. 2002; Nagendra et al.2011). This taxa

reported to occur abundantly in lower estuarine zone of

Ashtamudi estuary of southern Kerala coast (Nagendra

et al.2011), which is in close proximity to the Kollam

coastal plain. The water depth in the lower estuary of

Ashtamudi is about 4m and the predominant lithology

represented by sand. N.scaphum was associated with

A.beccarii in the middle estuarine zone (central and

western kayals) of Ashtamudi estuary where water

depth ranges from 2-4m and lithology mainly

represented by clay and silt. In the central and western

kayals of Ashtamudi abundance of A.beccarii and

N.scaphum account to about 60% and 25-30%

respectively.

The relative abundance of dominant taxa A.beccarii and

N.scaphum show opposite trends in upcore direction.

Ammonia beccarii shows gradual reduction in

abundance from 59.85% in lower litho unit 1, 56.1% in

litho unit 2 to 45.47% in upper litho unit 3, while N.

scaphum varies from 20% in litho unit1, 31.24% in litho

unit 2 and 0.67% in litho unit 3. The abundance ratios of

these two dominant taxa indicate that litho units 1&2

where lithology mainly silt and clay were deposited in

estuarine complex and further evolving into near shore

conditions in the upper litho unit3 and finally into

coastal plain from 3m to the top of the core. The sharp

break in lithology at 13m and predominance of sand

(lateritic) in litho unit 3 suggests high proportion of

riverine input.

5. Conclusions:

Based on litho content, the sediment core is divided

into 3 unit’s namely silty clay, clayey silt and sand

(lateritic) in stratigraphic order. The base of the

litho unit 3 marks a sharp break in litho content.

Thirty benthic foraminifer species belonging to 20

genera are identified. Q mode cluster analysis of 30

taxa recognized 8 biotopes which cluster at a

linkage distance 50.

A.beccarii and N.scaphum are abundant but exhibits

reverse trends in their abundance in upcore. Their

abundance ratios suggest gradual evolution of

environment from estuarine complex (litho units

1&2) to near shore (litho unit 3) and finally into

coastal plain.

6. Reference:

291 R GAYATHRI, R NAGENDRA, A N REDDY, P SATHIYAMOORTHY AND N SURESH



[1] Boltovskoy, Esteban, Giussani, Graciela,

Watanabe, Silvia and Wright, Ramil. 1980. An atlas

of Benthic Shelf Foraminifera of the Southwest

Atlantic Book, London, p147.

[2] Gandhi, S Rajamanickam, G.V.M. and Nigam, R.

2002 Taxonomy and distribution of benthic

foraminifera from the sediments off Palk Strait,

Tamil Nadu, East Coast of India. Journal of the

Palaeontological Society of India 47, 47-64.

[3] Gaudette, H., W. Flight, L. Toner & D. Folders.

1974 An inexpensive titration method for the

determination of organic carbon in recent

sediments. J. Sediment. Res., 44, 1, 249-253.

[4] Jayaraju, N. Reddy, K.R. 1996 Impact of pollution

on coastal zone monitoring with benthic

foraminifera of Tuticorin, southeast coast of India.

Indian Jour. Mar. Sci. 25:76-378

[5] Kumar. V., Manivannan,V. and Ragothaman, V.

1996 Spatial and temporal variations in

foraminiferal abundance and their relation to

substrate characteristics in the Palk Bay, off

Rameshwaram, TamilNadu. Proc. XV Indian Coll.

Micropal. Strati. pp. 393-402.

[6] Loeblich, A.R Jr Tappan, H. 1988 Foraminiferal

genera and their classification: New York, Van

Nostrand Reinhold Company, 970 pp.

[7] Mini Chandran.1988 Ecobiology of Sasthamkotta,

M.Sc. Dissertation, University of Kerala.

[8] Murray, J.W. 1971 an Atlas of British Recent

Foraminiferids. Heinemann Educational Books,

London.

[9] Nagendra, R., Prakash, T. N., Jayamurugan, K., R,

Gayathri, R and Reddy, A. N. 2011 A Preliminary

Reports on Benthic Foraminiferal Assemblage in

the Ashtamudi Estuary, Kerala. Journal of the

Palaeontological Society of India. V. 56 no. 2, pp.

137-142.

[10] Nair, K.M and Padmalal, D. 2004 Quaternary

geology and geomorphology of southern Kerala

sedimentary basin, West coast of India. In G.R.

Ravindra kumar and N.Subhash (Eds) Earth

System Sciences and natural resource management

pp.69-92

[11] Reddy, A. N. and Reddy, K.R. 1982 Recent

benthonic foraminifera from the Araniar River

Estuary, TamilNadu. Indian Journal of Marine

Sciences, v.11, pp.249-250.

[12] Ramanathan, R.M.1970 Quantitative differences in

the living benthonic foraminifera of Vellar estuary,

Tamil Nadu: Journal of Geological Society of India,

v.11 (2), pp.127-141.

Fig I: Location map of sediment core (BH-8) at Kollam, Kerala


South Kerala, India



Fig II: Vertical distribution of foraminifera taxa and dendrogram showing biotopes




Fig III: Vertical distribution of total foraminifera abundance, species diversity and lithological content in core

(BH-8) at Kollam area

Explanation of Plate I:

1. Bolivina nitida, Brady, 1884

2. Brizalina subaenariensis (Cushman, 1922)

3. Fursenkoina texturata (Brady 1884)

4. Rectobolivina? virgule (Brady)

5. Cancris oblongus, (Williamson, 1858)

6. Elphidium macellum (Fichtel & Moll, 1798)

7. Elphidium crispum (Linnaeus, 1758)

8. Ammonia beccarii (Linnaeus, 1758)

9. Operculina ammonoide, Sidebottom, 1918

10. Nonion scaphum (Fichtel & Moll)

11. Quinqloculina sp.

12. Ammonia dentata (Parker and Jones, 1865)

13. Nonion boueanum (d´Orbigny, 1846)

14. Elphidium hispidulum, Cushman, 1936

http://www.marinespecies.org/aphia.php?p=taxdetails&id=113011


South Kerala, India



PLATEI:




TableI: Vertical distribution of lithological variants, foraminifera biotopes and species diversity in Core

sedimentary section, Kollam area

Depth

(m) Lithology Sand% Silt% Clay% Biotopes TNF

Species

Diversity

0-3 Clayeysand 89.11 3.9 6.98 Ammonia beccarii 20 3

3 Clayeysand 70.09 11 18.91 Ammonia beccarii 107 7

4 Clayeysand 76.56 9.67 13.74 Ammonia beccarii 84 3

5 Sand 99.99 0 0 Ammonia beccarii 105 4

7 Sand 99.99 0 0 Ammonia beccarii 91 4

8 Siltysand 77.81 19.75 2.43 Nonion scaphum 58 7

9 Siltysand 78.9 15.55 5.46 Ammonia beccarii 183 8



12 Clayeysand 76.52 5.9 17.58 Ammonia beccarii 176 9

13 Clayeysand 75.01 7.82 17 Ammonia beccarii 284 13

14 Siltyclay 0.6 15.34 84.06 Ammonia beccarii 223 10












38 Siltyclay 0.76 29.53 69.41 Nonion scaphum 96 6

TFN= Total Number of Foraminifera

Total 2769


South Kerala, India



TableII: Spatio-temporal distribution of benthic foraminifera of Kollam coastal sediment

www.cafetinnova.org

Indexed in





ISSN 0974-5904, Volume 07, No. 01

February 2014, P.P.297-304


Solid Waste Transportation Cost Using Arm Roll in Malang City,

Indonesia

BURHAMTORO1,2

, ACHMAD WICAKSONO3, M BISRI

4 AND SOEMARNO

5

1Deparment of Environment and Development Studies, Graduate School, University of Brawijaya,

Malang, East Java, INDONESIA 2Department of Civil Engineering, Polytechnic of Malang, INDONESIA

3Department of Civil Engineering, Faculity of Engineering, University of Brawijaya, INDONESIA

4Departmen of Water Resources Engineering, Faculity of Engineering, University of Brawijaya, INDONESIA

5Laboratory of Remote Sensing, Faculity of Agriculture, University of Brawijaya, INDONESIA

Email: [email protected], [email protected], [email protected], [email protected]

Abstract. In the processes of wastes management, wastes transportation process requires 70-80% of the total cost of

wastes management, so that the cost savings can be done at the cost of freight. This study aims to create the

modeling of freight costs and the cost of transporting waste in Malang city with the Arm Roll system. The primary

data consisting of transporting distance, travelling time and volume of transported wastes, are obtained by a primary

survey on the vehicle movement of the Arm Roll. Secondary data consisting of transportation cost and vehicles

charateristics obtained from the Sanitation Department of Malang city and stakeholders. Estimation of the waste

transportation cost by the wastes freight cost equation obtained from the relationship between vehicle operating cost

(VOC) and vehicle speeds. Equation of the wastes transporting cost with the Arm Roll system is Y = 171,329.33x-

0.50. (Y is a transportation cost (IDR/m

3) and X is a vehicle speed (km/h)). Wastes transport use the Arm Roll at the

speed of 21,759 km/h requires a fee of IDR 36,698.92 /m3.

Keywords: Wastes, Arm Roll, speed of vehicles, Transport Cost.

1. Introduction:

Wastes management processes are divided into three

stages: wastes collection, wastes transportation and

wastes processing. Among these three stages, transport

costs reach about 70-80% of the total cost of wastes

management (Utami, 2008). The saving of wastes

management cost can be made on the cost of

transportation.

Processes of waste transport are conducted by the

wastes transporting vehicles or waste transportation

fleets serving at the temporary waste disposal stations

(TPS) to the final landfill site (TPA) (SNI T-13-1990-

F). The transporting wastes vehicles are grouped into

two types; namely the stationery container system (SCS

or Dump Truck) and the hauled containers systems

(HCS or Arm Roll) (Silvia, 2010). Each type of vehicles

has the required transporting cost (Levinson, 2005). The

waste transporting cost can be estimated using

calculation of vehicle operating costs (VOC) in the Arm

Roll system (Burhamtoro, 2013).

The Arm Roll waste transportation system serves waste

transport by leaving the truck tub at the TPS, Arm Roll

transporting garbage bins which have been fully filled,

without having to wait for wastes charging as well as

the Dump Truck (Hartanto, 2006). Malang city has 16

units of Arm Roll scattered on the five sub districts.

Arm Roll system deployment locations tend to have a

slight amount of wastes. Advantages of the Arm Roll

system is in a single day transporting waste can serve

more than one temporary disposal station (TPS).

Differences resulted in this transport systems should be

considered in calculating cost of waste transportation.

The vehicle operating costs (VOC) are costs that occur

in the economical operation of a vehicle under normal

conditions for a specific purpose (Hamidi, 2013). In

Indonesia there are two ways commonly used in the

calculation of vehicle operating cost (VOC), that are

PCI (Pacific Consultant International) and LAPI-ITB

(Institute for Research and Industry Affiliations –

Institute Technologi of Bandung). In the calculation of

vehicle operating cost (VOC) , one of its determining

factor is the speed of vehicle (Lavinson, 2005). Thus, in

calculating cost of waste transport should considering

the speed of waste vehicles.

Based on the above descriptions, it is known that the

Arm Roll waste transport system is different from the

Dump Truck Haulage system, so that the calculation of

the transporting costs is also different. This research





298 Solid Waste Transportation Cost Using Arm Roll in Malang City, Indonesia



simulated the cost of transporting wastes by The

ArmRoll in Malang city.

The Hauled Container System (HCS) is the waste

collection system, where the waste containers are

transported to the final landfill, it is emptied and then

returned to its original location or to the next locations

of wastes collection (SNI T-13-1990-F).

Fig1: Waste Transportation System HCS (Hauled

Container System)

The standard operating procedures of the HCS are:

a. The empty waste containers were taken from the

garage to the Polling station-1 (TPS-1).

b. The waste container has been fully filled from TPS-

1 is brought to the landfill to be emptied.

c. The Container that has been emptied is taken to

replace the container in the polling station-2 (TPS-

2).

d. The wastes containers which have been fully filled

from TPS-2 are brought to the Landfill to be

emptied, so onward.

e. The wastes truck back to the garage.

Basically, the cost of transportation is the amount of

money that must be paid by the transportation provider

to perform transport services for both fixed costs

(infrastructure) and variable costs (operational costs).

These costs depend on a variety of conditions associated

with geographic, infrastructure, administrative

boundaries, energy, and how they were brought (Sofyan

et al., 2009).

Various methods can be used to estimate the

relationship between output and costs, one of the

methods that have been used in the transportation

studies is the statistical method (Waters, 1997). The

waste transport cost method with the statistical approach

using the multiple regression model. Multiple regression

analysis shows how costs may change if one of the

factors is changed.

Variables used in the calculation of transport costs

include fixed costs and variable costs. Fixed costs are

costs that can not be changed. Fixed costs include

vehicle taxes, accident insurance, and a physical test of

vehicles accounted in a single year (Burhamtoro, 2012);

the variable costs consist of five components, i.e. cost of

tires, fuels, maintenance costs, labor costs and total

variable costs (Mark Berwick and Moh. Farooq, 2003).

Variable costs are affected by the speed of the vehicle

during transport. Velocity used is the speed of travel

(Journey Speed), obtained from the mileage travelled

divided by the time of service during the process of

transporting wastes (Burhamtoro, 2012)

The vehicle operating cost (VOC) is sum of the cost of

fuels, engine lubricants, tire, maintenance, depreciation,

interest rates, insurance, driver wages and overhead,

these are influenced by the speed of vehicles which is

the variable cost per 1,000 km (Yanagiya, 1990);

whereas according to Lavinson (2005) and Sugiyanto

(2011), calculation of variable costs should be carried

out per km.

The Vehicle Operating Cost calculations in this study

used the PCI method. This is the empirical model

developed since 1979 in the Jakarta Intra Urban

Feasibility Study, which is still used by the Jasa Marga

co. ltd. (Hamidi, 2013).

2. Research Method:

The research was carried out on the entire fleet of

trashes hauler Arm Roll that serves the Malang city. It

was conducted between September to December 2012.

The necessary data includes the travelled distance,

transporting time, volume of wastes which are

transported, and costs of other needs (tires, oil, spare

parts, vehicle services, etc.).

The data collection method used is primary data and

secondary data. Primary data is the data obtained by

field surveys. While secondary data was gathered from

the Sanitation Department in Malang and stakeholders.

Primary data includes vehicle motion patterns survey

conducted on board or follow the vehicle transport

process Arm Roll in the process of transporting waste

from Pool-TPS-TPA and back to the pool, to get the

data distance, time and the volume of waste. Secondary

data includes vehicle data, the price of tires, parts prices,

service vehicles, driver costs, etc. Speed data obtained

from the division between distance and time. Equation

of modeling the cost of transporting waste based on data

speed and the cost required.

The waste transport cost modeling is based on the

calculation of the operating costs of the Arm Roll

vehicles, vehicle operating costs are calculated by the

method of PCI that involve fixed costs and variable

costs. Variable costs are affected by the length of the

journey, while fixed costs are not influenced by the

length of the trip. The formulation of variable cost can

be seen in Table 1. The fixed cost is calculated based on

the value of the vehicle taxes, mandatory contributions,

299 BURHAMTORO, ACHMAD WICAKSONO, M BISRI AND SOEMARNO



and feasibility test of the vehicle divided by the number

of working days.

The result of the calculation is the transportation cost of

arm roll per day and per m3, not per trip because the trip

of each vehicle and each day is different. So that

transportation cost are general.

Fig2: Location of the temporary disposal station (TPS) in Malang city, East Java Indonesia

Table1: Variable Cost Calculation in Vehicle Operation Cost (VOC)

No. Parameter Cost Equations Informations

1 Fuel Cost (0,06427V2 - 7,0613V + 318,3326) x Fuel cost

Fuel Cost

(liter/1000km)

2 Oil cost (0,00048V2 - 0,05608V + 3,07383) x Oil Cost

Oil Cost

(liter/1000km)

3 Tire cost (0,0011553V - 0,0059333) x Tire cost x n Tire Tire Cost

(1 Tire/1000km)

4 Spare part cost (0,0000191V + 0,00154) x Vehicle price Spare part cost

(Spare part/1000km)

5 Service cost (0,01511V + 1,212) x Mechanic wages per hour Service Cost

(Mechanic/1000km)

6 Depreciation (1/(6,129V + 245)) x Vehicle price Depreciation cost

(Depreciation/1000km)

7 Interest rate ((0,12 x 1000)/(1750V)) x Vehicle price Interest rate

(interest rate/1000km)

8 Insurance ((0,06x1000x0,5)/(1750V)) x Vehicle price Insurance

(Insurance/1000km)

9 Drive wages (1000/V) x Driver wages Driver Wages

(Wage/1000km)

10 Overhead Total Cost x 10%

Information: V = Speed (km/hour), Source: Yanagiya, 1990

3. Results And Discussion:

3.1. Model of Transpotation Cost with Arm Roll:

Malang city uses two types of Arm Roll vehicles, ie

Arm Roll New Toyota Dyna WU 342 R TKMQ AD 3

four pieces and 11 pieces Arm Roll Toyota Dyna Rino

BY 43. VOC calculations for the two types of Arm roll

method approach Yanagiya (1990). Limited to the speed

of velocity variations are allowed in the city is up to 50

km / h (Peraturan Menteri Perhubungan No. 14, 2006).




The costs are calculated in the calculation of vehicle

operating cost (VOC) covers the cost of fuel, oil, tires,

parts, service, depreciation, interest rates, insurance, and

driver wages. The charges are obtained based on survey

to the market prices.

Figure 3 shows the relationship between the

transportation costs and vehicle speed for each type of

Arm Roll vehicle.

Table2: Speed and waste volume data

No. Vehicle Type Speed -carry

away (km/h)

Speed -empty

(km/h)

Waste

Volume (m3)

1 2 3 4 5

1 TOYOTA NEW DYNA WU 342 R TKMQ AD 3 21.23 19.94 8.68




5 TOYOTA DYNA RINO BY 43 21.09 22.70 8.97











Toyota New Dyna WU 342R

y = 1.410.684,24x-0,50

R² = 0,97

Toyota New Dyna Ryno

y = 1.642.837,23x-0,50

R² = 0,97

-

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

900,000

0

10

20

30

40

50

60

Co

st

(ID

R/d

ay

)

Speed(Km/hour)

TOYOTA NEW DYNA WU 342 R TKMQ AD 3

TOYOTA NEW DYNA RYNO BY 43

Fig3: The relationship between the transportation cost and the vehicle speed for each type of Arm roll

Graph in Figure 3 describes the function of the

corresponding equation which is the type of power. The

equation of the function of each type of vehicles;

Toyota New Dyna WU 342R has equation Y =

1,410,684.24x-0,50

with value R square 0.97. Toyota

New Dyna Rino equation Y = 1,642,837.23x-0,50

with R

square 0.97. Where that equation Y is vehicle

operational cost and X is speed.

Based on these equations, it can be seen that the faster

the vehicle, the smaller the costs charged. When using

speed in the city is 27 km / h then the cost to Toyota

Dyna WU 342R is IDR 271,486.31, while the Toyota




Dyna New Ryno is IDR 316,164.17. Toyota Dyna WU

342R costs less IDR 44,677.86 or 14.13% of Toyota

Dyna Ryno

Relationship with the vehicle speed and transportation

costs Arm Roll type as in Figure 4.

y = 1,522,341.81x-0.50

R² = 0.91

0

100000

200000

300000

400000

500000

600000

700000

800000

900000

0

10

20

30

40

50

60

Co

st

(ID

R/d

ay

)

Speed (Km/hour)

ARM ROLL

Fig4: The relationship between the transportation cost and the ArmRoll speed

The regression model is Y = 1,522,341.81x-0.50

with R

square 0.91. The chart is calculated based on the data

speed of the whole arm roll with vehicle operating costs

required.

Calculated based on the cost of transporting waste cost

divided by the volume of waste transported. The large

volume of waste transported obtained from a survey

vehicle movement. The relationship between the speed

chart with transport costs can be seen in Figure 5.

Toyota New Dyna WU 342Ry = 195.115,39x-0,50

R² = 0,97

Toyota New Dyna Rynoy = 150.442,97x-0,50

R² = 0,97

-

20,000

40,000

60,000

80,000

100,000

120,000

0

10

20

30

40

50

60

Co

st

(ID

R/m

3)

Speed(Km/hour) Gambar Grafik Hubungan Kecepatan Kendaraan Arm Roll

Terhadap Biaya Pengangkutan Sampah

TOYOTA NEW DYNA WU 342 R TKMQ AD 3

TOYOTA NEW DYNA RYNO BY 43

Fig5: The relationship between transportation costs and the Arm Roll speed

Based on Figure 5. Toyota New Dyna WU 342R has

the equation Y = 195,115.39x-0,50

. while for vehicles

Toyota Dyna BY obtained function equation Y =

150,442.97x-0,50

, where Y in the equation is the cost of

transport per m3. while the X variable is the speed of the

vehicle in each equation with R2 of 0.97.

When calculating the cost of transporting waste to the

equation above, using the speed in the city is 27 km / h

then the cost to Toyota Dyna WU 342R is IDR

37,549.97, while the Toyota Dyna New Ryno is IDR

28,952.76. In the calculation of the cost of transporting

the Toyota Dyna WU 342R is more expensive IDR

8,597.21 or 29.69% than Toyota Dyna Ryno.

Modeling the cost of transporting waste by Arm roll is

calculated based on cost of transportation of any type of

vehicles. Cost model arm roll transporting waste types

are shown in figure 6.




y = 171,329.33x-0.50

R² = 0.81

-

20,000.00

40,000.00

60,000.00

80,000.00

100,000.00

120,000.00

-

10

20

30

40

50

60

Co

st

(ID

R/m

3)

Speed(Km/hour)Gambar Grafik Hubungan Kecepatan Kendaraan Arm Roll

Terhadap Biaya Pengangkutan Sampah

Arm Roll

Fig6: The relationship between the transportations costs and the ArmRoll speed

Based on the cost of transporting waste chart

relationships with Arm Roll speed, then the equation Y

= 171.329,33x-0,50

with the magnitude of R2 is 0.81.

3.2. The Freight Cost Based on Existing Speed:

Vehicle speed is a determining factor in the calculation

of vehicle operating costs (Yanagiya. 1990). Vehicle

speed is obtained from the distance trips devided by the

travelling time of services. Meanwhile, the volume of

transported wastes is obtained from the average of the

wastes volume every kind of vehicle type ArmRoll over

a certain period of time as shown in Table 3.

Table3: Speed and volume of waste in each brand of vehicle

N

o. Vehicle Type

Avg. speed

(km/h)

Avg.

(km/h)

Vol. Waste

(m3)

Avg.

(m3)

Distnc.

(km)

Avg.

(km)

1 Toyota New Dyna WU 342R 21.23

21.96

8.68

7.23

94.70

77.30 2 Toyota New Dyna WU 342R 23.55 6.01 72.13

3 Toyota New Dyna WU 342R 22.09 5.75 69.37

4 Toyota New Dyna WU 342R 20.95 8.47 73.02

5 Toyota Dyna Rino By 43 21.09

21.63

8.97

10.92

88.78

90.40

6 Toyota Dyna Rino By 43 20.79 10.21 89.47










Table 3 points out that the type of Toyota Dyna Rino BY 43 has the fastest speed: 21.63 km / h. Toyota New Dyna

WU 342 R reaches up to 21.96 km / h. If the speed is included in the cost of transporting waste equation. the

following results are obtained;




Table4: Freight costs under the existing speeds

Arm Roll Type Equation Speed (Km/hour) Freight Cost (IDR/m3)

T. New Dyna WU 342R 195.115,39x-0,50

21.63 41,953.02

T. New Dyna Rino BY

43 150.442,97x

-0,50 21.96 32,103.75

Arm Roll 171.329,33x-0,50

21.795 36,698.92

Based on Table 4. it can be seen that the cost of

transporting wastes using Arm Roll for Toyota New

Dyna Rino is cheaper than Toyota New Dyna WU 342

R. Cost of transporting waste to the Toyota New Dyna

Rino BY 43 IDR 32,103.75 / m3. While Toyota New

Dyna WU 342 R amounting to IDR 41,953.02 / m3. The

cost of transporting waste in Malang using Arm Roll

requires cost of IDR 36,698.92 / m3.

4. Conclusion:

The model of waste transport equation with Arm Roll

system is Y = 171,329.33x-0.50

(Y is a transportation cost

(IDR/m3) and X is a vehicle speed (km/h)). The cost of

transporting by Toyota Dyna WU 342R is more

expensive 29.69% of Toyota Dyna Ryno. In Malang

city, cost of transporting waste by using the Arm Roll

system cost IDR 36,698.92 /m3 at the existing vehicle

speed of 21.795 km/h.

5. Acknowledgement:

The authors sincerely acknowledge the modifications

recommended by the reviewer, Dr. Gito Sugiyanto, ST.,

MT. at 27th

Sept. 2013 as Associated Profesor of Civil

Engineering in Faculty of Science and Engineering,

Jenderal Soedirman University Purwokerto, Indonesia

and Dr. Nindyo Cahyo Krisnanto, ST., MT. at 1st Nov

2013 as Lecture of Transportation at Civil Engineering

Department, Faculty of Science, Janabadra University,

Jogjakarta, Indonesia. Special thanks for the Indonesian

Directorate General of Higher Education which has

funded this research.

6. Reference:

[1] Apaydin. O. and Gonullu. M.T. “Route Optimation

for Solid Waste Colllection:Trabzon (Turkey) Case

Study”. Global NEST Journal. Volume No. 9 (1):6-

11. 2007.

[2] Arifin. M. Z.. Gagoek Soenar Prawito and Dwi

Ramdhani. “Analisa Efektifitas Fasilitas Zebra

Cross Pada Jalan MT. Haryono dan Jalan

Gajayana”. Jurnal Rekayasa Sipil. Volume 1(1):13-

24. 2007.

[3] Ayres Frank JR. PH.D. Matematika Universitas

Edisi 3. Erlangga. 2006.

[4] Burhamtoro. “Optimizing of Transportation

Municipal Solid Waste at Malang City”.

Interdisciplinary Research in Natural Resources and

Environmental Management. Post Graduate

Progam University of Brawijaya. Pp.77-78. 2012.

[5] Burhamtoro. “Biaya angkut Hauled Container

System (HCS) dan Stationery Contaioner System

(SCS) Pada Pengangkutan Sampah Rumah Tangga

(Studi Kasus: Kecamatan Blimbing Kota Malang)”.

Jurnal Prokons. Volume 6 Nomor 1 pp. 26-37.

2012.

[6] Burhamtoro. Model of Municipal Solid Waste

Transportation Costs Type Dump Truck (Case

Study At Malang City. Indonesia). International

Journal of Engineering & Technology IJET-IJENS

Vol: 13 No: 03. 2013.

[7] Departemen Pekerjaan Umum SK SNI T -13 -1990

– F tentang Tata Cara Pengelolaan Teknik Sampah

Perkotaan.

[8] Hamidi Gede Wajib. “Analisis Biaya Perjalanan

Akibat Tundaan Lalu Lintas”. Jurnal Ilmiah

Elektronik Infrastruktur Teknik Sipil. Volume 2.

No. 1. 2013.

[9] Hartanto W. “Kinerja Pengelolaan Sampah di Kota

Gembong Kabupaten Kebumen”. Magister Teknik

Pengembangan Wilayah dan Kota, UNDIP,

Semarang. 2006.

[10] Hummels. David. “Transportation Costs and

International Trade in the Second Era of

Globalization”. Journal of Economic Perspectives.

Volume 21. pp. 131–154. 2007.

[11] Lavinson. D. Corbett Michael. and Hashami

Maryam “Operating Costs for Trucks”. Twin

Cities: Department of Civil Engineering University

of Minnesota. Minnesota. 2005.

[12] Mark Berwick and Moh. Farooq. Truck Costing

Model for Transportation Managers. Upper Great

Plains Transportation Institute North Dakota State

University. North Dakota. 2003.

[13] Sofyan. M. Saleh. Ade Sjafruddin. Ofyar Z. Tamin

and Ruzz Bona Frazila. “Pengaruh Muatan Truk

Berlebihan Terhadap Biaya Pemeliharaan Jalan”.

Jurnal Transportasi. Volume 9 (1): 85-96. 2009.

[14] Saxena. Shikha. “Sustainable Waste Management

Issues in India”. The IUP Journal of Soil and Water

Sciences. Volume 3 (1):72-90. 2010.

[15] Silvia Gabrina T. A.A. Jaya Wikrama. Nyoman

Karnata Mataram and Arya Ngurah M. W. “Analisa

Angkutan Persampahan di Kecamatan Kuta”.

Jurnal Ilmiah Teknik Sipil. Volume 14 (2): 208-

217. 2010




[16] Sugiyanto. Gito. “Estimation of Congestion Cost of

Motorcycles Users in Malioboro. Yogyakarta.

Indonesia”. International Journal of Civil &

Environmental Engineering (IJCEE-IJENS).

Volume 11 (01):56-63. 2011.

[17] Utami Beta Dwi. Nastiti Siswi Indrasti and Arya

Hadi Dharmawan. “Pengelolaan Sampah Rumah

Tangga Berbasis Komunitas: Teladan dari Dua

Komunitas di Sleman dan Jakarta Selatan”. Jurnal

Transdisiplin Sosiologi. Komunikasi. Dan Ekologi

Manusia. Volume 2 [1]; pp 49-68. 2008.

[18] Yanagiya. Kensuke. “Feasibility Study on The

Cikampek-Cirebon Toolway Project”. Japan

International Coorporation Agency. 1990.

[19] Peraturan Menteri Perhubungan No.14/ 2006

tentang Manajemen dan Rekayasa Lalu Lintas di

Jalan.

[20] Peraturan Menteri Pekerjaan Umum

No.21/PRT/M/2006 tentang Kebijakan dan Strategi

Nasional Pengembangan Sistem Pengelolaan

Sampah (KNSP-SPP).

www.cafetinnova.org

Indexed in





ISSN 0974-5904, Volume 07, No. 01

February 2014, P.P.305-312


Structural Health Monitoring Techniques in Civil Engineering: An

Overview

BHAVANA PATEL S S1, KATTA VENKATARAMANA

1, K S BABU NARAYAN

1, BHAGYASHRI PARLA

2

AND YUKINOBU KIMURA3

1Department of Civil Engineering, NITK, Surathkal, Mangalore 575025, INDIA

2Department of Civil Engineering, Government Engineering College, Ramnagar, INDIA

3Department of ocean Civil Engineering, Kagoshima University, JAPAN


Abstract: Structural Health Monitoring (SHM) is an emerging and promising technology for safety and integrity of

structures. Vibration Based Monitoring (VBM) has gained more importance in the field of civil engineering as

damage parameters are sensitive to vibration. This paper presents brief introduction on SHM and VBM. Traditional

and advanced techniques adopted for damage identification, localization and quantification by various authors have

been discussed. However it is still a challenging task for the researchers to develop a technique which gives efficient

and reliable solution for a particular Structure.

Keywords: Structural Health Monitoring, Vibration Based Monitoring, Damage, Identification, Localization,

Quantification.

Introduction:

Increased importance for safety and economy has given

more prominence for SHM from the past two decades.

Early detection of damages avoids catastrophic failure

and aids in providing necessary support for its

prolonged working condition. Visual inspection can

detect damages, if they are large enough to see through

the eyes and located in the accessible region. This

becomes complicated while monitoring large structures,

in which damages are present in the inaccessible region.

In such situations effective nondestructive techniques

such as SHM can be used to monitor the integrity of

structural systems.

SHM is a scientific process of non-destructively

monitoring the health of the system. This is carried out

in three phases: a) Monitoring the operational /

environmental load that acts on the system; b) System

diagnosis, to identify, locate and quantify the extent of

damage that occurs in the system due to loading; c)

System prognosis, i.e., to predict the present and future

performance of the system in the presence of damage(s).

Advent of SHM has replaced traditional periodic

maintenance by condition based maintenance, which in

turn reduces the down time and labour cost. During the

extreme events such as earthquake, accidents and

blasting SHM has been used to determine the extent of

damage on the structural components and also to check

its adequacy for the occupation. One of the major

drawbacks of this system is to convince the customers

to use SHM since it is still in the developmental stage.

Fig.1 by Ma T W et al. (2005) shows the procedure

Carried out in SHM.

Fig1: Procedure in SHM

VBM is the commonly adopted method for monitoring

civil structures. This method relies on global parameters

for damage identification, localization and

quantification. Structural damage alters dynamic

306 Structural Health Monitoring Techniques in Civil Engineering: An Overview



characteristics such as stiffness, mass and damping

which in turn changes frequency, mode shapes and

damping ratio. The change in these parameters depends

on location, nature and severity of the damage.

Advent of new sensors has made SHM an efficient and

robust technology for monitoring the structural systems.

The use of sensors like piezoelectric, Fiber optics,

magnetostrictive and other sensors have given rise to

many advanced techniques which are effective in

locating and quantifying damage in the structural

damage.

Literature Review:

Various damage identification, localization and

quantification methods using VBM have been reviewed

by many researchers. Doebling et al (1996) discussed on

the application oriented technological development, by

discussing the critical issues related to monitoring of the

structures. Chang et al (2003) have given the recent

developments in the field of SHM in advanced countries

and various sensors adopted are listed. Hsieh et al.

(2006) discussed on the factors affecting damage and

sensor selection based on the parameter. Sohn et al

(2004) have discussed on various parameters affecting

damage, selection of such features and their extraction.

Sohn also gives information about different types of

sensors, excitation methods, and application of damage

detection methods in the real time structures. Humar et

al. (2006) have presented the description of some of the

simple structures, on which monitoring can be done.

They conclude by stating vibration based methods are

completely not reliable for monitoring. Brownjohn et

al.(2011) have given a brief introduction on SHM,

comparison of the early vibration based monitoring and

present day vibration based damage detection is given.

Further case studies on mainly bridges and other

structures are presented. Paper is concluded with the

recommendation for the vibration based monitoring.

Traditional Methods:

In traditional methods there are mainly two domains:

one is time and another frequency domain. Damage

detection in frequency domain is carried out by

comparing the response of healthy and unhealthy

structure. Some of the damage sensitive parameter in

this domain is frequency, mode shape, modal curvature,

stiffness and damping ratio. Sheinman (1996) has

developed an algorithm for damage detection and also

for updating mass and stiffness matrices based on the

minimum static and/or dynamic modes. Johnson et

al.(2004), Yuen et al.(2004) and Ragland et al.(2010)

have discussed the damage identification based on the

dynamic parameters stiffness, frequency and mode

shapes, also different types of damages are studied.

Escobar A. et al (2005) have located and quantified

damage in two and three dimensional analytical models

by using transformation matrix which is used to obtain

reduced stiffness matrix. Nayeri et al. (2007) have

discussed the advantage of using natural excitation

technique with Eigen system realization algorithm for

the evolution of the modal properties. The natural

frequencies are correlated with time for damage

localization and quantification. Nayeri et al. (2009)

improved the same technique by considering degree of

freedom as the reference for selection of modes. Blames

E. et al. (2008) have worked on non-parametric damage

identification algorithm based on mode shapes, using

averaging operation to smoothen the temperature effect.

Esmaeel et al. (2011) have used Energy damage index

for arriving at empirical modal decomposition used for

damage localization algorithm.

Salawu (1997) has given a review on structural

assessment using frequency changes which is one of the

sensitive, easily and cheaply available responses.

Salawu has concluded frequency alone can’t be the

criteria for the health assessment of a structure. Hwang

and Kim (2003) have discussed on damage localization

and its severity. This have been carried out by using

frequency response function (FRF) of tests and

analytical, which is used for model updating. Mal et al

(2005), Catbas et al. (2006) & Golafshani et al.(2010)

identify damage location using damage correlation

index, modal flexibility and Minimum rank

perturbation respectively obtained from frequency

response function (FRF). Cury & Borges (2010) have

discussed the damage localization and quantification

using strain and frequency data.

In time domain displacement, amplitude and

acceleration are considered as damage sensitive

parameters. It is very difficult to obtain the accurate data

and also difficult to get the physical meaning of these

data. Catbas et al. (2007) have discussed limitations of

damage identification using time response data on large

scale structures. Yang & Sun (2010) have discussed the

static based method of localization and quantification of

damage in the structural systems. Adbo (2012) has

extended Yang and Sun work using displacement

curvature. Sanayei et al. (2012) have discussed damage

detection on bridges using strain measurements. He

conducts tests in three methods on a bridge structure i.e.

conventional, NDE data and model updating using

FEM.

Sohn & Farrar (2000), and Lei et al.(2003) have

proposed damage localization using time series of

vibration signals of auto regressive models. Ma et al.

(2005) have located the damage using time domain for

linear system. Quantification has been carried out by

using system identification in an iterative way. Carden

and Brownjohn (2008) have discussed the damage

307 BHAVANA PATEL S S, KATTA VENKATARAMANA, K S BABU NARAYAN,

BHAGYASHRI PARLA AND YUKINOBU KIMURA



detection in time domain using Autoregressive Moving

Average (ARMA) models. Zhang et al.(2008) have

discussed damage identification of linear systems using

Support Vector Regression (SVR) data processing

technique. Further Trendafoilova et al.(2009) have

introduced the idea of using larger amplitude vibrations

in the time domain, and this can be adopted for the

nonlinear structures. Wang et al.(2011) have discussed

the monitoring of bridges in time domain using flutter

and buffeting analysis. Gao et al. (2002) have developed

an algorithm for damage localization using damage

localization vector (DLV) where inducing stress field

has zero magnitude in the damaged region. This method

is carried out using flexibility matrix. Sim et al. (2011)

have extended the work with acceleration and strain

data.

Advanced methods:

Impedance method uses high frequency vibrations and

electromechanical coupling property of sensor, by

observing the changes in impedance damages are

detected. Advancement of the sensor technology has

increased in the usage of impedance based damage

identification. When piezoelectric transducers are

stressed it generates electric field and in turn mechanical

strain. This electrical response can be used to detect

damage through phase shift or magnitude chance. Fig.2

shows impedance model diagram by Peairs et al.

(2007).The major advantages of this method are low

power consumption, piezoelectric transducers are small

in size and they are model dependent. Park et al. (2000)

have experimentally applied impedance based damage

detection algorithm for composite reinforced concrete

wall. This method is validated with other commonly

adopted methods. Peairs et al. (2007) have carried out

damage detection using electro-mechanical Impedance

method. Since high frequencies are used in this method,

spectral finite element method has been used and further

these results are validated using experimental data.

Fig2: Impedance model

Statistical Pattern recognition method can be divided

into four steps, operational evaluation, Data acquisition

and cleansing, feature selection and data compression

and statistical model development. Features like modal

properties, flexibility, time and frequency domain

responses are being considered. Farrar C. et al. (1999)

and Yao and Pakzad (2012) have carried literature

survey on statistical pattern recognition method. Further

Yao and Pakzad have proposed model spectra and

residual auto correlation for damage detection. Sohn and

Farrar (2000) have used statistical process control

technique for damage diagnosis, by using auto

regressive models acceleration time histories are

measured. Altunok et al. (2007) have adopted a non-

statistical approach known as possibility approach. This

is independent of damage feature, do not require

probabilistic knowledge. Fig. 3 by Yao R and Pakzad S

N (2012) shows the general procedure for statistical

process control.

Fig3: Statistical Process Control

System identification uses statistical method for

developing the mathematical models. Koh et al. (2006)

have developed a system identification (SI) system

using stiffness matrix. Incomplete stiffness matrices are

obtained from the system and further using

condensation model damage identification algorithm is




developed. Gul and Catbas (2008) have discussed SI

based on complex mode indicator function coupled with

random decrement for identifying modal parameters.

Unscaled flexibility and deflection profiles of healthy

and unhealthy structures are used for damage

assessment. Hsieh K. et al. (2008) have proposed

damage detection method based on Spectral finite

element method with the combination of SI, finite

element method and model updating process. Natural

frequency shift and mode shape change is used as

damage indicating parameter. Das A. et al. (2012) have

proposed damage identification based on iterative least

squares extended Kalman filter. The procedure uses

finite element method based time domain system

identification for damage identification.

A wavelet is the extension of the Fourier series where a

signal is demonstrated only using time domain where as

in wavelet analysis a signal can be used in time as well

as frequency domain, hence wavelet analysis has gained

more importance in the recent years. It is also very

sensitive to the singularities caused by the sudden

change in stiffness and masses of the structure. The

software like matlab provides a good built in program

for wavelet transforms. Khatam H. et al. (2007) have

carried out damage identification process by using

wavelet transformation. The sudden change in the

spatial variation of the transformed response helps in

identifying and locating the damage. Bouboulas and

Anifantis (2010) have discussed on nonlinear dynamic

equation, which are solved using incremental iterative

procedure. Parametric studies are carried out to identify

the sensitivity of vibration behaviour. The derived time

response is analyzed using Fast Fourier Transform,

Continuous Wavelet Transform and Discrete Wavelet

Transform. Chanpheng T. et al (2012) have also

proposed a damage detection method using Degree of

Non linearity (DON) as the parameter for earthquake

excitation. DON can be obtained based on the ground

motion and vibration of the structure, Hilbert’s

transform is used for analysis. Pai and Sundaresan

(2012) have discussed on damage identification on thin

wall using dynamic based methodology. Boundary

effect evaluation, operational deflection shapes and

conjugate pair decomposition are used for the analysis

of space-wave number and time-frequency domain.

Short time Fourier transform, Hilbert-Hung transform

are adopted.

Neural networks are the computational models which

works based on the inputs provided to the system. The

connected set of processing units is called as neurons.

These connected sets are trained with the available static

or dynamic responses using forward or back

propagation algorithm. Further these trained data are

tested for workability using test data. The capability of

the NN depends upon the input data. The training is

repeated till it gives satisfactory performance. Fig. 4 by

Jeyasehar and Sumangala (2006) show the ANN

Schematic representation for back algorithm. Jeyasehar

and Sumangala (2006) have developed Artificial Neural

Network (ANN) based approach for monitoring the

structure. This Technique is based on the stiffness and

frequency, training and the test data are generated by

conducting experiments on damaged and undamaged

structures. Zapico and Gonzalez (2006) have used

frequencies and developed and algorithm using ANN.

Mass sensitivity has been considered for damage

identification of 4 storeys building excited using seismic

excitation. Figueiredo E et al. (2010) have discussed

damage identification in operational and environmental

variation conditions using ANN.

Fig4: ANN Back Propagation

Banks et al. (1996) has discussed on damage detection

and localization by parameterized partial differential

equations and Galerkin approximation technique. This

is one of the inverse optimization techniques which use

the changes in the mass density, elastic modulus and

damping ratio. Jaishi and Ren (2005) have given a finite

element model updating technique. Dynamic parameters

are studied and Guyan reduction technique has been

adopted for model upgradation. Chandrashekhar and

Ganguli (2009) have developed an optimization

algorithm for damage localization and quantification

using Fussy Logic System (FLS). Modal curvature

changes are observed using Gaussian fussy sets and

Mapped for location.

Genetic algorithm (GA) is a natural selection process

based on the Darwin’s evolution theory. An initial

population is chosen then process of selection, genetic

operation and replacement is carried out until the

convergence criteria are achieved. Caicedo and Yun

(2010) have developed an algorithm for identifying





local and global minima for model updating. This has

been achieved through steady state GA. Meruane and

Heylen (2010) have developed an optimization

technique called Parallel Genenetic Algorithm (PGA).

This is an advantage over the GA as it is fast and

simpler to solve. Srinivas et al. (2010) have developed

an evolutionary algorithm for localizing the damage and

thereby reducing the parameters in the objective

function. Further using genetic algorithm damage has

been quantified.

Development of small, autonomous and easily hand-

able sensing system becomes important for effective

monitoring. Micro-Electro-Mechanical systems are one

such technology which measures the physical

parameters. Sensor prototypes are developed for

sensing, processing, communication and actuation of the

structural system. These sensor prototypes communicate

wirelessly by forming Wireless Sensor Networks

(WSN). Wireless system most commonly employed in

military, life science, robotics and so on. Chacon et al.

(2009) used WSN for acquiring strain data. Ramos L. et

al. (2010) have monitored wirelessly using modal

analysis. The results obtained using WSN is compared

with the conventional frequency change method and

reliable results are obtained. Quinn W. et al. (2011) has

described the design and performance analysis under

replicated site condition. Wireless sensors with

temperature and humidity measuring capacity are used

for monitoring the structure.

Some of the other techniques adopted by researchers are

presented. Bayissa and Haritos (2007) have given

damage detection based on bending moment response

power mass spectral density (MSD) in two dimensional

plate structures. The total energy output under bending

MSD gives mean square value (MSV), Damage index is

derived from MSV. Damage indices, normalized

damage index, MSV curvature and relative root mean

square error are used for damage identification and

localization. Xu et al. (2011) has proposed a new

stochastic damage detection method. Probability density

function (PDF) of structural stiffness has been obtained

by integrating statistical moment based method with

probability density evolution for the damaged and

undamaged structure. Xue et al. (2009) have adopted

auxiliary particle filtering method to track the stationary

system for their sudden change in parameters due to the

presence of damage. In this method density is proposed

as a mixture density that depends upon the past state and

most recent observation.

Wave based methods have gained more importance as

they aid in damage localization with their directional

properties. Scalea et al. (2003) have adopted guided

stress waves for stress monitoring and damage

identification in the strands. Magnetostrictive

transducers are used in the experiments for excitation

and sensing. Medda and DeBrunner (2009) have used

frequency and time analysis using local vibration

characteristics which are sensitive to the damage. The

novel beam forming technique has been adopted, in

which array of beam generated from the transducer

produces ultrasonic waves to scans the region. Further

Wavelet packet sub band signals are used to produce

energy map, using frequency as parameter for damage

location. Nucera and Scalea (2011) have discussed a

monitoring of steel strands using ultrasonic guided

waves. The relation between the guided waves and the

amplitude of the strands are derived to detect the

damage. Experimental and numerical studies on this

procedure are discussed. Further Climent et al.(2011)

have proposed a method in which Acoustic emission

energy and history of plastic strain energy was

calculated from acceleration and displacement

measurements. Correlation exists between energy

dissipation and plastic deformation; hence a tentative

formula is derived.

To improve the robustness of the present day

monitoring strategies, researchers have combined two

methods and proved to obtain reliable results. Hua et al.

(2009) have proposed static based optimization problem

for damage detection on cable stayed bridges. Forces

are redistributed in different strand and further using

model updating damages are localized. Lautour and

Omenzetter (2010) have given an algorithm with

combination of Auto regression (AR) models and ANN.

AR models are used to obtain the acceleration time

series data from the experiments. Coefficients of AR are

considered as the damage sensitive parameters. ANN is

trained for damage classification. Loh C. et al. (2011)

have extended Lautour and Omenzetter work for

application on dams. Xu et al (2011) have proposed the

method for parameter identification and damage

detection using displacement measurement. Further

Neural network is adopted for both parameter and

system identification. Root mean square and root mean

square of the prediction displacement difference vector

is used for evaluation. Yu et al. (2012) have proposed a

crack monitoring method by dual mode sensing. It is

based on Acoustic emission and ultrasonic wave

propagation technique. It is promising technique as it

does not require the past performance of the system.

Conclusions:

This paper provides the review on techniques of damage

identification, localization and quantification in SHM.

All the described methods are successful in damage

localization and quantification with their own

limitations. Environmental disturbances and size of the

damage plays a major role in structural monitoring.

Recent development of hybrid techniques has shown




promising results but research has to be carried out to

bring out much efficient technique. It is still a challenge

for the research community to say that one single

technique that can be applied to all the structures under

all conditions. Hence, it should be well understood that,

the methods developed in SHM are application based or

specific to a particular component and is not

generalized. Hence, opportunities are available to look

for different damage sensitive parameters or damage

identification techniques to be adopted.

Acknowledgements:

The financial support from the Technical Education

Quality Improvement Programme (TEQIP) for this

research work is gratefully acknowledged.

1. Reference:

[1] Abdo M A-B(2012), Parametric Study of Using

Only Static Response in Structural Damage

Detection, Engineering Structures, Vol. 34, pp.124-

131.

[2] Altunok E, et al (2007), Probabilistic Approach for

Damage Detection in SHM, Journal of Structural

Engineering, Vol.133, pp. 1247-1256.

[3] Balmes E, et al (2008), Merging Sensor Data from

Multiple Temperature Scenarios for Vibration

Monitoring of Civil Structures, Structural Health

Monitoring Journal, Vol. 7, No. 2, pp. 0129.

[4] Banks, H T et al (1996), An Experimentally

Validated Damage Detection Theory in Smart

Structures, Journal of Sound and Vibration, 191(5),

pp. 859-88.

[5] Bayissa W L, and Haritos N (2007), Damage

Identification in Plate-like Structures Using

Bending Moment Response Power Spectral

Density, Structural Health Monitoring Journal, Vol.

6, No. 1, pp. 0005.

[6] Bouboulas A S, and Anifantis N K (200), Finite

Element Modeling of a Vibrating Beam With a

Breathing Crack: Observations on Crack Detection,

SHM Journal, Vol.0, No.2, pp. 131-145.

[7] Brownjohn J M W, et al (201), Vibration-based

Monitoring of Civil infrastructure: Challenges and

Success, Journal of Civil SHM, Vol.1, pp.79-95.

[8] Caicedo J M and Yun G (2010), A Novel

Evolutionary Algorithm for Identifying Multiple

Alternative Solutions in Model Updating, SHM

Journal, Vol. 10, No.5, pp. 491-501.

[9] Carden E P and Brownjohn J M W, (2008), ARMA

Modelled Time-Series Classification for SHM of

Civil Infrastructure. Mechanical Systems and

Signal Processing Journal, Vol. 22, pp.295-314.

[10] Catbas F N, et al (2006), Use of Modal Flexibility

for Damage Detection and Condition Assessment:

Case Studies and Demonstrations on Large

Structures, Journal of Str. Eng. , Vol.32, pp.1699-

1712.

[11] Catbas F N, et al (2007) Limitations in Structural

Identification of Large Constructed Structures,

Journal of Str. Engineering, Vol.133, pp.1051-

1066.

[12] Chacon R, et al (2009), Wireless Sensor Networks

for Strain Monitoring During Steel Bridges

Launching, SHM Journal, Vol.8, No.3, pp. 0195-

11.

[13] Chandrashekhar M and Ganguli R, (2009),

Structural Damage Detection Using Modal

Curvature and Fuzzy Logic, Structural Health

Monitoring Journal, Vol. 8, No. 4, pp. 0267-16.

[14] Chang P C, et al (2003), Review Paper: Health

Monitoring of Civil Infrastructure, Structural

Health Monitoring, Vol. 2, No.3, pp.0257-267.

[15] Chanpheng T, et al (202), Nonlinear Features for

Damage Detection on Large Civil Structures Due to

Earthquakes, Structural Health Monitoring, Vol.11,

No.4, pp. 482-488.

[16] Climent A B, et al (2011), An Acoustic Emission

Energy Index for Damage Evaluation of Reinforced

Concrete Slabs Under Seesmic Loads, Structural

Health Monitoring, Vol.1, No.1, pp. 69-81.

[17] Cury A A, et al (200), A Two-step, Technique for

Damage Assessment Using Numerical and

Experimental Vibration Data, Structural Health

Monitoring Journal, Vol.10, No.4, pp.47-428.

[18] Das A K, et al (202) Health Assessment of Large

Two Dimensional Structures Using Limited

Information: Recent Advances, Advances in Civil

Engineering, Article ID 582472, 16 pages.

[19] Doebling, S W (1996), Damage Identification and

Health Monitoring of Structural and Mechanical

Systems from Changes in Their Vibrational

Characteristics: A Literature Review, Laboratory

report, Los Alamos, New Mexico.

[20] Escobar, J A et al (2005), Structural Damage

Detection Using Transformation Matrix, Computers

and Structures Journal, No. 83 pp. 357-368.

[21] Esmaeel R A, et al (2011), Computational

Simulation and Experimental Verification of a New

Vibration-based Structural Health Monitoring

Approach Using Piezoelectric Sensors, Structural

Health Monitoring, Vol., No.2, pp. 237-250.

[22] Farrar, C R (1999), A Statistical Pattern

Recognition Paradigm for Vibration-Based

Structural Health Monitoring, Proc. 2nd

International Workshop on SHM, Stanford CA Sept

8-10.

[23] Figueiredo E, et al (2010), Machine Learning

Algorithms for Damage Detection Under

Operational and Environmental Variability,

Structural Health Monitoring Journal, Vol.10, No.6,

pp.559-572.





[24] Gao, Y (2002), Flexibility-Based Damage

Localization Employing Ambient Vibration, 15th

ASCE Engineering Mechanics Conference,

Columbia University, New York, June 2-5.

[25] Golafshani A A, et al (2010), Health Monitoring of

Structures Using Few Frequency Response

Measurements, Transaction A; Civil Engineering,

Vol.7, No.6, pp. 493-500.

[26] Gul M and Catbas F N (), Ambient Vibration Data

Analysis for Structural Identification and Global

Condition Assessment, Journal of Engineering

Mechanics, Vol. 134, pp.650-662.

[27] Hsieh K H (2006), Overview of Vibrational SHM

with Representative Case Studies, Journal of Bridge

Engineering Vol.11, pp. 707-715.

[28] Hsieh K H (2008), Structural Damage Detection

Using Dynamic Properties Determined from

Laboratory and Field Testing, Journal of

Performance of Constructed Facilities, Vol.22, pp.

238-244.

[29] Hua X G, et al (2009), Structural Damage

Detection of Cable-Stayed Bridges Using Changes

in Cable Forces and Model Updating, Journal of

Structural Engineering, Vol.135, pp.1093-1106.

[30] Humar J, et al (2006), Performance of Vibration-

Based Techniques for the Identification of

Structural Damage, SHM Journal, Vol. 5, no. 3, pp.

215.

[31] Hwang, H Y and Kim, C (2004), Damage Detection

in Structures Using a Few Frequency Response

Measurements, Journal of Sound and Vibration,

Vol. 270, pp. 1-14.

[32] Jaishi B and Ren W (2005) Structural Finite

Element Model Updating Using Ambient Vibration

Test Results, Journal of Str. Eng., Vol.131, pp.617-

628.

[33] Jeyasehar and Sumangala (2006), Non-destructive

Evaluation of Prestressed Concrete Beams Using an

(ANN) Approach, SHM, Vol. 5, No. 4, pp.313.

[34] Johnson E A, et al (2004), Phase I IASC-ASCE

Structural Health Monitoring Benchmark Problem

Using Simulated Data, Journal of Engineering

Mechanics, Vol.130, pp.3-15.

[35] Khatam H, et al (2007), Harmonic Class Loading

for Damage Identification in Beams Using Wavelet

Analysis, SHM Journal, Vol. 6, No. 1, pp. 0067.

[36] Koh C G, et al (2006), Condensed Model

Identification and Recovery for Structural Damage

Assessment, Journal of Structural Engineering,

Vol.132, pp.208-226.

[37] Lautour O R and Omenzetter P (2010), Damage

Classification and Estimation in Experimental

Structures Using Time Series Analysis and Pattern

Recognition, Mechanical Systems and Signal

Processing, Vol.24, pp.1556-1569.

[38] Lei, Y et al (2003), An Enhanced Statistical

Damage Detection Algorithm Using Time Series

Analysis, 4th

International Workshop on SHM,

Standford, USA, September 15-17.

[39] Loh C H, et al (2011), Application of Advanced

Statistical Methods for Extracting Long-term

Trends in Static Monitoring Data from an Arch

Dam, SHM, Vol.10, No. 6, pp.587-601.

[40] Ma T-W, et al (2005), Structural Damage Diagnosis

and Assessment Under Seismic Excitations, Journal

of Eng. Mechanics, Vol.131, pp. 1036-1045.

[41] Mal A, et al (2005), A Conceptual Structural Health

Monitoring System Based on Vibration and Wave

Propagation, Structural Health Monitoring Journal,

Vol. 3, No. 4, pp. 283.

[42] Medda A and DeBrunner (2009), Near-field Sub-

band Beam forming for Damage Detection in

Bridges, SHM Journal, Vol. 8, No. 4, pp. 0313-17.

[43] Meruane V and Heylen W (2010), Damage

Detection With Parallel Genetic Algorithms and

Operational Modes, Structural Health Monitoring

Journal, Vol.9, No.6, pp.481-16.

[44] Nayeri R D, et al (2007), Application of SHM

Techniques to Track Structural Changes in

Retrofitted Building Based on Ambient Vibration,

Journal of Engineering Mechanics, Vol.133, pp.

1311-1325.

[45] Nayeri R D, et al (2009), Study of Time-Domain

Techniques for Modal Parameter Identification of a

Long Suspension Bridge with Dense Sensor Arrays,

Journal of Eng. Mechanics, Vol.35, pp. 669-683.

[46] Nucera C and Scalea F L (201), Monitoring Load

Levels in Multi-wire Strands by Nonlinear

Ultrasonic Waves, SHM, Vol. 0, No.6, pp.617-629.

[47] Quinn W, et al (2011), Design and Performance

Analysis of an Embedded Wireless Sensor for

Monitoring Concrete Curing and Structural Health,

Journal of Civil SHM, Vol.1, pp. 47-59.

[48] Pai and Sundaresan (2012), Space-wavenumber and

Time-frequency Analysis for Damage Inspection of

Thin-walled Structures, Structural Health

Monitoring, Vol.11, No. 4, pp.452-471.

[49] Park G (2000), Impedance-based Health

Monitoring of Civil Structural Components, Journal

of Infrastructure Systems, Vol. 6, pp. 153-160.

[50] Peairs D M, et al (2007), Circuit Analysis of

Impedance-based Health Monitoring of Beams

Using Spectral Elements, Structural Health

Monitoring Journal, Vol. 6, No. 1, pp. 81.

[51] Ragland W S, et al (2010), Finite Element

Modeling of a Full-scale Five-girder, Bridge for

Structural Health Monitoring, Structural Health

Monitoring Journal, Vol.10, No.5, pp. 449-465.

[52] Ramos L F, et al (2020), Operational Modal

Analysis of Historical Constructions Using




Commercial Wireless Platforms, Structural Health

Monitoring Journal, Vol.10, No.5, pp.511-521.

[53] Salawu, O S (1997), Detection of Structural

Damage Through Changes in Frequency: A

Review, Eng. Structures Journal, Vol. 19, No.9, pp.

718-723.

[54] Sanayei M, et al (2012), Instrumentation, Non-

destructive Testing and Finite-Element Model

Updating for Bridge Evaluation Using Strain

Measurements, Journal of Bridge Engineering,

Vol.17, pp. 130-138.

[55] Scalea F L, et al (2003), Stress Measurement and

Defect Detection in Steel Strands bu Guided Stress

Waves, Journal of Matr. Civil Eng., Vol.15, pp.29-

227.

[56] Sheinman, I (1996), Damage Detection and

Updating of Stiffness and Mass Matrices Using

Mode Data, Comp. & Str. Journal, Vol.59, No.1 pp.

149-56

[57] Sim S H, et al (2011), Multimetric Sensing for

Structural Damage Detection, Journal of

Engineering Mechanics, Vol.137, pp. 22-30

[58] Sohn, H et al (2004), A Review of Structural Health

Monitoring Literature: 1996-2001, Laboratory

Report, Massachusetts Institute of Technology, Los

Alamos National Laboratory.

[59] Sohn, H et al (2001), Damage Diagnosis Using

Time Series Analysis of Vibration Signals, Smart

Matr. and Structures Journal, Vol. 10 pp. 446-451.

[60] Sohn H, and Farrar C R (2000), Statistical Process

Control and Projection Techniques for Structural

Health Monitoring, European COST F3 Conference

on System Identification & Structural Health

Monitoring, Madrid, Spain June 6-9.

[61] Srinivas V, et al (2010), Multi-stage Approach for

Structural Damage Identification Using Modal

Strain Energy and Evolutionary Optimization

Techniques, Structural Health Monitoring Journal,

Vol.10, No.2, pp.219-230.

[62] Trendafoilova I, et al (2009) An Investigation on

Vibration-based Damage Detection in Circular

Plates, SHM Journal, Vol. 8, No.4, pp. 0291-12.

[63] Wang H, et al (), Comparison of Ambient Vibration

Response of the RunYang Suspension Bridge

Under Skew Winds with Time-Domain Numerical

Prediction, Journal of Bridge Engineering, Vol.16,

pp. 513-526.

[64] Xu B, et al(2011), Damage Detection for a Frame

Structure Model Using Vibration Displacement

Measurement, SHM, Vol. , No.3, pp. 281-292.

[65] Xu Y L, et al (2011), Stochastic Damage Detection

Method for Building Structures with Parametric

Uncertainties, Journal of Sound and Vibration,

Vol.330, pp. 4725-4737.

[66] Xue S, et al (2009), Structural Damage Detection

Using Auxiliary Particle Filtering Method, SHM

Journal, Vol. 8, No. 2, pp.0101-12.

[67] Yang Q W and Sun BX (2010), Structural Damage

Localization and Quantification Using Static Test

Data, SHM Journal, Vol.0, No.4, pp.381-389.

[68] Yao R and Pakzad S N (2012), Autoregressive

Statistical Pattern Recognition Algorithms for

Damage Detection in Civil Structures, Mechanical

Systems and Signal Processing, Vol.31, pp. 355-

368.

[69] Yu L, et al (2012), Dual Mode Sensing With Low

Profile Piezoelectric Thin Wafer Sensors for Steel

Bridge Crack Detection and Diagnosis, Advances

in Civil Engineering, Article ID 402179, 10 pages.

[70] Yuen K-V, et al (2004), Two-Stage Structural

Health Monitoring Approach for Phase I

Benchmark Studies, Journal of Mechanical

Engineering, Vol. 30, pp. 16-33.

[71] Zapico J L, and Gonzalez M P (2006), Numerical

Simulation of a Method for Seismic Damage

Identification in Buildings, Engineering Structures

Journal, Vol.28, pp. 255-263.

[72] Zhang J, et al (2008), A Pattern Recognition

Technique for Structural Identification Using

Observed Vibration Signals: Linear Case Studies,

Eng. Structures Journal, Vol. 30, pp. 1439-1446.

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ISSN 0974-5904, Volume 07, No. 01

February 2014, P.P.313-319


Dynamic characteristics of a cable-stayed pedestrian and cyclists

footbridge 120 m long

IZABELA J MURZYN Institute of Structural Mechanics, Faculty of Civil Engineering, Cracow University of Technology, POLAND


Abstract: The paper presents dynamic characteristics, i.e. natural frequencies and modes of vibrations of a cable-

stayed pedestrian and cyclists footbridge over the Raba River, Southern Poland. The total length of the suspended

structure is 120 m. The dynamic analysis was carried out with the ABAQUS software. Four variants of a numerical

model were created on the basis of different types of finite element (shell, solid and beam) used for modeling the

pylons, plate and girder. The results revealed that the natural frequencies are relatively low and could coincide with

the frequency of pedestrian steps (walking or running) causing the resonance phenomenon.

Keywords: Footbridge, Dynamic characteristics of footbridges, Numerical modeling of Footbridges, ABAQUS.

1. Introduction:

Footbridges are objects of public space which primary

use is to carry pedestrians over an obstacle [7]. But, in

recent decades, footbridges have become peculiar

symbol of the development of civil engineering.

Nowadays, footbridges are site-specific designs which

give their users the opportunity to enjoy advantages of

environment they are located in.

Footbridges should be designed according to the

recommendations of the standards which require an

analysis of the superstructure in the two limit states:

ultimate limit state (ULS) and serviceability limit state

(SLS). These objects significantly differ from the

conventional bridges, especially as far as their influence

on the users is considered. The pedestrians walk directly

on the deck, staying there longer time than while

traveling by car across the bridge. Hence, they directly

feel the structure behavior. At the design stage the

footbridges require special attention to ensure the proper

functional features and comfortable use.

Application of advanced materials, technology and

calculation techniques makes currently designed

footbridges to longer, lighter and more slender than

older ones. The new approach has an contrary effect on

the dynamic properties of footbridges. Sometimes, the

lowest natural frequency of a structure coincides with

the frequency of pedestrian steps (while walking or

running). This situation may cause a resonance

phenomenon and contribute to the failure of the

structure. Failures of bridges, caused by excessive

vibrations in former times, have been reported by

historical sources [7, 10]. The bridge in Broughton, UK,

collapsed due to the march of 60 soldiers. This event

resulted in putting on bridges boards warning the troops

to break step when crossing [10]. It should be noted

that not only the pedestrians are a source of dynamic

load for footbridges. These structures can also be

exposed to kinematic excitation originating from

seismic or paraseismic phenomena, like mining tremors.

From recent history of civil engineering the most

famous example was the Millennium Bridge located in

London. Its dynamic problems that emerged during its

opening day on 10 June 2000 attracted the scientists’

attention and underlied more than 1000 articles and over

150 broadcasts in the media around the world [10].

2. The dynamic characteristics of footbridges:

Usually the first step in the dynamic analysis is to

determine the dynamic characteristics of the structure,

i.e. mode shapes and natural frequencies of a structure.

It could be carried out by performing the solution of the

classical eigenvalue problem of a general multi-degree

of freedom structure presented in formula [4]:

(1)

where: – mass and stiffness matrix, respectively

– displacement vector. Assuming the linear work

of the structure matrices and are independent of .

Formula (1) is a homogeneous system of differential

equations with constant coefficients. After a series of

simple transformations [8] equation (2) has the form:

(2)

where: – vector of amplitudes, – angular

frequency.


314 Dynamic characteristics of a cable-stayed pedestrian and cyclists footbridge

120 m long



The recognition of the dynamic characteristics of a

footbridge is the extremely important issue in the

analysis of broad sense of limit states of these objects,

such as: comfort, possibility of resonant excitation and

load-bearing capacity or deformation of the structure.

Many works were dedicated to the evaluation of the

dynamic characteristics of pedestrian bridges [1, 2, 7,

and 10]. The vast majority of these researches are based

on the results of in situ tests. During the tests various

forms of excitation were used: random vibrations,

harmonically forced vibration, stochastically forced

vibration.

The basic natural frequency as a function of the span of

the footbridge is shown in Fig. 1 for 67 pedestrian

bridges from different parts of the world [1]. In Figure

1 the critical range of frequencies from 1.4 to 2.4 Hz are

clearly marked. These are typical frequencies of vertical

vibrations caused by walking pedestrians [1, 7, and 10].

Fig1: The basic natural frequency as a function of the span structure. Results for 67 pedestrian bridges [1]

3. Geometry and material data of the footbridge:

The calculations of the dynamic characteristics were

performed for an existing footbridge (Fig. 2) located in

Pcim, Southern Poland. The primary purpose of the

structure is to carry pedestrians and cyclists across the

Raba River. It is a part of the road junction situated

within 300 m of the national expressway S7.

The suspended structure consists of three spans: the

middle one is 60.00 m long, whereas two extreme are

25.50 m long. The total theoretical length is 120 m.

The footbridge’s plat is composite with steel girders.

The modulus of elasticity of steel girders and cross-bars

was taken as 210 GPa. The Poisson's ratio was assumed

as 0.29. The superstructure has been suspended from

steel pylons 11.80 m high. The pillars and abutments are

founded on reinforced concrete piles with a diameter

100 cm. The cross-section of the footbridge is shown in

Fig. 3, whereas the side view of the object and its main

dimensions are presented in Fig. 4.

300

340

200 70 70

2% 2%

IPE 220 IPE 360

resin epoxy-polyurethane 3 mm

reinforced concrete plate 15 - 18 cm

Fig2: The cross-section of the footbridge [9]

315 IZABELA J MURZYN



Fig3: Footbridge in Pcim, Southern Poland [9]

17º

2550 6000 2550

1200 1350 1200 1200 1200 1200 1200 1350 1200

11100

200 200

450 450

1180

Raba

Fig4: Side view and main dimensions [cm] of the footbridge in Pcim [9]

The footbridge is equipped with elastomeric bearings as

linking elements between the deck and the piers.

Usually, a two-coefficient Mooney-Rivlin model is used

as a constitutive model of hyperelastic nonlinear

elastomeric material of bearings. However, the

parameters of the Mooney-Rivlin material: C10 and C01

can be replaced with equivalent elasticity modulus:

E = 6 (C10 + C01). In this paper the parameters of the

Mooney-Rivlin model, assumed as C10 = 0.292 MPa and

C01 = 0.177 MPa [3], were replaced with the equivalent

elasticity modulus 2.814 MPa. Such simplification is

commonly used in calculations of bridges with

elastomeric bearings [3, 6]. The Poisson’s ratio of

elastomeric material was taken as 0.49.

4. Numerical model of the footbridge:

The analysis of the dynamic characteristics of the

footbridge was carried out with the ABAQUS which is

a package of programs for solving complex engineering

problems. ABAQUS is composed of modules in which

is defined the next stage of modeling: Part (module

responsible for defining geometry); Property (module

responsible for determining characteristics of material);

Assembly (combination of the model and creation a set

of parts); Step (configuration procedures for analysis

and expected results); Load (load application and

determination of boundary conditions); Mesh (module

in which is generated mesh); Job (creation of task and

send it for analysis); Visualization (overview of

analysis).

The fixed boundary conditions, reflected the high

rigidity of the foundation rock, were applied at the end

of the piers. The Lanczos algorithm implemented in

Abaqus software was used for the solution of

eigenproblem, as a powerful tool for a quick solution of

tasks of a large size [8]. The FE model of the footbridge

along with some details of the structure is shown in Fig.

5.


120 m long



Fig5: The FE model of the footbridge

Four Variants of the numerical model were created. The

differences in the models concerned finite elements

which were used for modeling particular parts of the

footbridge, i.e. pylons, plate, grids and cross-bars (see

Table 1). In all Variants of the model abutments and

pillars modeled by 3D Solid elements, whereas steel

cables attached to pylons and abutments were modeled

by Truss elements. It has been assumed that the

structure works in linear-elastic range. The material

parameters and geometry have been adopted on the

basis of technical design [9].

Table1: Characteristics of four variants of the model

Variant

of model Pylon Plate Grid

Variant 1 Beam Shell Beam

Variant 2 Beam Solid Beam

Variant 3 Shell Shell Shell

Variant 4 Shell Solid Shell

The assembling of all parts (Assembly) is an important

step in creating a FE model in the ABAQUS package.

In all variants of the model of the footbridge parts were

connected by Constraints. The idea of this solution is to

partially or fully eliminate degrees of freedom of a

group of nodes and couple their motion to the motion of

a master node. In summary, the four models of the

footbridge consisted of four types of elements: Beam,

Truss, Solid and Shell as well appropriate types of

constraints.

The models of footbridge were created by four types of

elements: Beam, Truss, Solid and Shell. In all Variants

of the model appropriate types of Constraints were used

considering the differences in the number of degree of

freedom at nodes of shells, beams and solids.

For all variants of the models connection of the steel

cable with abutments (and pylons) was implement by

Kinematic Coupling type. It was defined as constrained

degrees of freedom: U1=1, U2=1, U3=1, UR1=0,

UR2=0, UR3=0 (where: U – translational DOF, UR –

rotational DOF). It is a good solution for elements

which lack in rotational stiffness connected to solid

elements (see Fig. 6).

Fig6: Connection of the steel cable with abutments

(Kinematic Coupling)

In Variant 3 and 4 of the model in case to assembly

Shell elements with Solid elements a Shell-to-solid

coupling type was used. This solution is very useful in

situations in which local modeling requires 3D solid

elements but other parts of the structure can be modeled




as shells. The idea of this coupling is to link the motion

of a line of nodes along the edge of the shell part to the

motion of a set of nodes on a solid surface. The

connection of the pylon with pillar (Shell-to-solid

Coupling) as well as the cross-section of the pylon

(constructed as a box section) is presented in Fig. 7.

Fig7: Connection of pylon with pillar (Shell-to-solid

Coupling) and cross-section of pylon

In the ABAQUS package fully constrained contact

behavior is defined by using Tie constrains – it is a

simple way to permanently bond surfaces (from

different parts) together. It also should be noted that Tie

function guarantees easy mesh transitioning. In the

model the elements of the abutments were connected

together by Tie function. The example of such

connection with marked slave and master surface is

presented in Fig. 8.

The size of the numerical task for all Variants of the

model is summarized in Table 2. The size of the task

was described by “Number of nodes” and “Number of

elements”.

Fig8: Connection elements of abutments (Tie)

Table2: The size of the task

Variant

of model

Number

of nodes

Number

of elements

Variant 1 116945 79794

Variant 2 790589 475710

Variant 3 159839 113375

Variant 4 1641320 1032595

5. Dynamic characteristics of the footbridge:

As a result of the modal analysis the natural frequencies

and mode shapes of the footbridge were obtained. The

value of first three natural vibration frequencies is

collected in Table 3.

Table3: Natural vibration frequencies for different

Variants of the model

Variant

of model

Natural frequency [Hz]

1 2 3

Variant 1 1.90 2.10 2.64

Variant 2 1.89 2.08 2.59

Variant 3 1.94 2.10 2.63

Variant 4 1.87 2.15 2.67

The modes of vibration for Variant 3 of the model are

presented in Figs 9-13. It should be emphasized that for

all Variants modes of vibration have the same shape.

Fig9: The first mode of natural vibration (1.94 Hz)

Fig10: The second mode of natural vibration (2.10 Hz)


120 m long



Fig11: The third mode of natural vibration (2.63 Hz)

Fig12: The fourth mode of natural vibration (4.54 Hz)

Fig13: The fifth mode of natural vibration (4.72 Hz)

The first natural frequency is accompanied with vertical

mode of vibration (see Fig. 9). It falls into the critical

range of vibration frequencies caused by walking

pedestrians (see Fig. 1). Hence, the resonance

phenomenon may occur due to marching pedestrians.

The second natural mode is lateral (see Fig. 10). Since

in case of lateral vibration the critical range of

frequencies is from 0.5 Hz to 1.2 Hz, the second

frequency of 2.1 Hz is located outside critical range.

Finally, the third natural frequency (2.63 Hz) is

connected with vertical mode. Although is beyond the

critical range of frequencies of vibrations caused by

walking pedestrians, but it equals the frequency of

vibrations caused by running people. This type of

dynamic impact on the footbridge may also lead to the

resonance.

6. Conclusions:

The dynamic characteristics, i.e. natural frequencies and

modes of vibration of a cable-stayed pedestrian and

cyclists footbridge over the Raba River, Southern

Poland were evaluated. On the basis of the results the

following conclusions may be formulated:

- The results of the dynamic analysis of the

footbridge revealed that the natural frequencies of

the structure are relatively low. They could

coincide with the frequency of pedestrian steps

(walking or running) which may result in the

resonance phenomenon;

- The dynamic characteristics were obtained for four

Variants, which consisted of various types of finite

elements. Obtained results, both: natural

frequencies and modes of vibration, were very

similar for all Variants;

- The dynamics of footbridges is the extremely

relevant issue. Recognition of the dynamic

characteristics of footbridges is important while

analyzing limit states of these objects in a broad

sense, such as: comfort, possibility of resonant

excitation and load-bearing capacity or deformation

of the structure.

It should be pointed out that the evaluation of the

dynamic characteristics of the footbridge was carried

out on the basis of the numerical modeling of the

structure. However, a numerical model always presents

idealization of a real structure. Hence, a model and a

structure could differ as far as physical properties are

concerned. In order to perform a more detailed dynamic

analysis and to assess more reliable results it is

recommended to perform in situ research. The results

will also enable the validation of the calculation model

(e.g. by using the Modal Assurance Criterion).

7. Reference:

[1] Bachmann H., Ammann W., (1987), Vibrations in

Structures Induced by Man and Machines.

Structural Engineering Documents, International

Association of Bridge and Structural Engineering

(IABSE), Zurich, vol. 3e.

[2] Bachmann H., (2002), Lively Footbridges – a Real

Challenge, Proc. 1st International Conference on

”Design and dynamic of footbridges – Footbridge

2002”, Paris.

[3] Buckle I., Nagarajaiah S., Ferrell K., (2002),

Stability of Elastomeric Isolation Bearings:

Experimental Study, Journal of Structural

Engineering 128 (1): 3-11.

[4] Clough R.W., Penzien J., Dynamics of structures,

New York, 1993.




[5] Dulińska J., (2010), Evaluation of Dynamic

Characteristics of Masonry Arch Bridges: Linking

Full-Scale Experiment and FEM Modeling

Advanced Materials Research, 133-134: 605-610.

[6] Dulińska J., Szczerba R., (2013), Simulation of

dynamic behaviour of RC bridge with steel-

laminated elastomeric bearings under high-energy

mining tremors, Key Engineering Materials, 531-

532: 662-667.

[7] Flaga A., The footbridges, WKŁ, Warsaw, 2011.

[8] Hughes T., the Finite Element Method, New York:

Dover Publications, 2000.

[9] Murzyn I.J., Pańtak M., (2013), the vibration

comfort criteria assessment for the cable-stayed

pedestrians and cyclists footbridge in Pcim,

Engineering and Construction, 9: 493-496.

[10] Zivanovic S., Pavic A., Reynolds P., Vibration

serviceability of footbridges under human-induced

excitation: a literature review, Sheffield, 2003.

www.cafetinnova.org

Indexed in





ISSN 0974-5904, Volume 07, No. 01

February 2014, P.P.320-324


Experimental studies on the effects of corrosion on the flexural

strength of RC beams

POORNACHANDRA PANDIT1, KATTA VENKATARAMANA

1, K S BABUNARAYAN

1, BHAGYASHRI

PARLA2 AND YUKINOBU KIMURA

3

1Department of Civil Engineering, National Institute of Technology Karnataka,

Surathkal, Mangalore 575025, INDIA 2Department of Civil Engineering, Government Engineering College, Ramnagar, INDIA

3Department of ocean Civil Engineering, Kagoshima University, JAPAN


Abstract: RC structures are generally very durable and are capable of withstanding a variety of adverse

environmental conditions. However, failures of these structures still occur and reinforcement corrosion is one of the

major causes. In the present research, corroded Ordinary Portland Cement (OPC) beams were tested in the

laboratory to evaluate their flexural behavior. Accelerated corrosion technique was adopted to corrode the beams.

The corrosion was measured using Applied Corrosion Monitoring (ACM) instrument. From the results, it is seen

that, as the rate of corrosion increases, the load carrying capacity decreases. The deflection increases initially and

then decreases. It is observed that the stiffness of the beams is reduced when rate of corrosion is increased due to

changes in the modulus of elasticity of corroded steel.

Keywords: Accelerated Test, Corrosion rate, Load-Deflection curve.

Introduction:

Corrosion of steel reinforcement is a major cause of

degradation of RC structures. The corrosion process

leads to several coupled effects: cracking of concrete

cover due to expansive corrosion products; steel cross-

section reduction; and the degradation of steel–concrete

bond. As a result of these effects, the service life and the

load-bearing capacity of RC elements are considerably

reduced. The studies become more important especially

in corrosive environment such as coastal regions, where

structures are exposed to the environment having high

humidity and salt content. The moment carrying

capacity of an under-reinforced concrete beam depends

mostly on the strength of reinforcing steels. Therefore,

loss of reinforcing steel may be critical and requires

special consideration.

Corrosion is one of the important causes of steel area

loss (Castro et al. 1997). General corrosion, which

appears uniformly along the length of the reinforcement,

will have two effects: firstly, it will reduce the cross-

sectional area of the steel and secondly, it will create

local discontinuities in the steel surface. These effects

reduce the tensile capacity of the steel in proportion to

the loss of its cross-sectional area. Thus, as the

corrosion products increase, the cross-sectional area of

steel decreases and hence, in addition to the bond

deterioration, the ultimate moment capacity of structure

also decreases, till the area of the steel becomes so small

that it can no longer withstand the load and hence

results in the collapse of the structure (Ahmad 2003).

Reinforcement corrosion causes deterioration of

concrete structures in a chloride environment; affects its

durability and service life of Reinforced Concrete (RC)

structures (Cabrera 1996, Glass 2003, Poupard et al.

2006 and Zhang et al. 2011). Various prediction models

have been developed to predict the service life of

concrete structures (Ahmad 2003 and Jung et al. 2003).

Such methods usually consist of a theoretical method

combined with an empirical approach. However, most

of these methods are very difficult to apply because

often too many parameters are unknown (Liang et al.

1999).The basic problem associated with the

deterioration of reinforced concrete, due to

reinforcement corrosion is not that the reinforcing steel

itself is reduced in mechanical strength, but rather that

the products of corrosion exert stresses within the

concrete which cannot be supported by the limited

tensile strength of concrete, and therefore it cracks. This

leads to a weakening of the bond and anchorage

between concrete and reinforcement which directly

affects the serviceability and ultimate strength of

concrete elements within a structure. In addition, due to

tensile stresses developed during corrosion, existing fine

cracks and micro cracks in the surrounding concrete

tend to enlarge and form a network of interconnected

321 POORNACHANDRA PANDIT, KATTA VENKATARAMANA, K S BABUNARAYAN,




cracks. In the present study the corrosion rate is

measured using Applied Corrosion Monitoring (ACM)

instrument which is a non-destructive testing method.

Experimental Program

Concrete Mix design:

The mix proportion used for M20 grade of OPC concrete

was 1:2.24:3.67 and water cement ratio has been taken

as 0.5. The slump obtained was between 80 to 120mm.

Reinforcement details:

Reinforced concrete cantilever beams tested were of

cross section 300mmx400mm and 2150mm in length.

These beams had a shear span of 1750mm and bearing

length of 400mm. Beams with an effective cover of

30mm were designed based on the sizes of the

components in RCC Building as per IS 456:2000.

Ordinary Portland Cement (OPC) was used. Two

20mm diameter and one 16mm diameter TMT bars

were provided at top and same reinforcement was

provided at bottom. Shear reinforcement consisted of

12mm diameter TMT bars with a spacing of 150mm c/c

for a length of 1350mm from the free end, and with a

spacing of 75mm c/c for a length of 800mm for

remaining length of beam to ensure that flexural failure

would dominate over shear failures shown in the Fig.1.

Accelerated Corrosion:

In this experiment the electrochemical corrosion

technique is used to accelerate the corrosion of steel

bars embedded in concrete. Direct current is impressed

on the bar embedded in the specimen using an

integrated system incorporating a small direct power

supply with an output of 64V and 10Amps to monitor

the current. After specimens were immersed in a 5%

NaCl solution for a day to ensure full saturation

condition, the direction of current was arranged so that

the steel bars in the specimen served as the anode. The

stainless steel plate used as a cathode was placed along

the length of beam (Yoon et al2001). This arrangement

ensured a uniform distribution of corrosion current

along the whole length of the bar. A schematic

representation of the test set-up is shown in Fig.2. To

obtain the desired levels of reinforcement corrosion, the

current intensity and the electrifying time were

controlled (Ahmad 2009).

Corrosion Rate Measurements using Guard Ring:

These beam specimens were divided into number of

grids to place the guard ring probe to polarize the

definite area on concrete rebar as shown in Fig.3. At

each node, corrosion current density was measured by

LPR technique. These beam specimens were tested with

the corroded beams for different levels of corrosion.

The current density for each specimen is shown in Table

1. The established method of measurement uses a

galvanostatically controlled guard ring device. In this

method the reinforcing steel is polarised

potentiostatically by an inner auxiliary electrode and the

real time plot of the current response is displayed on a

laptop computer which controls the guard ring device.

The area of steel polarised is confined by a current

applied from an outer guard ring electrode which is

controlled by two sensor electrodes positioned between

the inner auxiliary and outer guard ring electrode (Law

et al., 2000). The potential between the two sensor

electrodes is frequently monitored, and the current

output from the guard ring electrode varied to maintain

a constant potential difference between the two sensor

electrodes. The method has been validated on an

electrical test circuit simulating active and passive

reinforcement corrosion. It is a non-destructive tool

used to measure corrosion current density in (mA/cm2).

Test Setup:

Flexural testing of the cantilever beam was carried out

under the specially prepared loading frame. Loading set

up was constructed in the existing reaction bed at

laboratory to test the beam as a vertical cantilever by

applying point load at the free end of the beam in

transverse direction. To achieve the fixity at the fixed

end of the beam, heavy duty hydraulic jack was used

against the steel column section at the other side of the

beam. Full fixity was achieved at the bottom end of the

beam by adjusting the movement of the hydraulic jack

arm.

All beams are tested as cantilever beam in a 15 tonne

capacity steel testing frame made up of rolled steel

joists, the beam having a span of 1750mm was fixed at

one end for a bearing length of 400mm. The span and

load points were kept constant for all the beams. The

concentrated load is applied on the free end of a beam.

The load spreader arm, wherever used is a rolled steel

joist which is supported on the rollers kept on the

loading points. Over the load spreader arm the proving

ring of 20 tonnes capacity which is used to measure the

applied load, is placed over which the hydraulic jack of

20 tonnes was fixed to the rolled steel joist of the

loading frame. The pump of a hydraulic jacks operated

by a hand lever. Fig.4 shows the loading details of the

beam specimen.

Theoretical Calculations:

Ultimate moment capacity of beam:

'416.0 ddCxdCM usuucu

3202.462)416.0(543.0 uucku xdbxfM

mmNMu 610154.128

Therefore, theretical ultimate load

322 Experimental studies on the effects of corrosion on the flexural strength of

RC beams



Pu = = N36

1023.731750

10154.128

Where,

Mu = Ultimate moment (N-mm)

Cuc =Compressive force in Concrete

Cus =Compressive force in Steel

d = Effective depth (mm)

Xu = Depth of Neutral axis (mm)

d' = Effective cover (mm)

l = Effective length (mm)

Deflection calculation for beam specimen

∂ =

effEI

PL

3

3

4

max 10950.8 uP mm54.65

Where,

P = Load (N); Pu=Ultimate Load (N)

L = Effective length (mm)

E = Modulus of elasticity of concrete

(N/mm2)

Ieff = Moment of inertia (calculated as per IS 456- 2000

Annex C) (mm4)

Results and Discussions:

For different rates of corrosion of OPC beams, the load

deflection relationship for control beams initially varied

linearly up to 80kN then started varying non linearly

(Fig.5). The control beams failed at an average load of

92kN. The average beam-end deflection was 60.96mm.

2.5% Corroded beams failed at 4.8% lesser load than

control beams (CB) and beam-end deflections increased

by 17.9% compared to CB. 5% corroded beams failed at

6.81% lesser load than CB and beam-end deflections

increased by 7.3%. 7.5% corroded beams failed at

27.6% lesser load and beam-end deflections decreased

by 6.5% compared to CB. 10% corroded beams failed at

30.6% lesser load and beam-end deflection decreased by

22.7% compared to CB.

As per the above observations, the stiffness of the

beams is reduced when rate of corrosion is increased.

For corrosion level up to 5%, with increase in rate of

corrosion the beams exhibit large deflection compared

to control beam specimens. The reason for such

behaviour is the change in the modulus of elasticity of

steel as the corrosion rate is increased. Due to this

effect, beams generally failed at lesser load with more

deflection compared to control beams.

Concluding Remarks:

It was observed that the control beams attained the

highest load carrying capacity of about 23% more

compared to theoretical ultimate load.

As the rate of corrosion increases, the load carrying

capacity of OPC beams decreases. For 2.5%, 5%, 7.5%

and 10% of corrosion level the reduction is by 4.8%,

6.8%, 27.6% and 30.62% respectively.

As the rate of corrosion increases the deflections of the

beams of OPC for 2.5% and 5% corrosion increases by

18%, 7.3% respectively, but for the 7.5% and 10%

corrosion, it decreases by 6.1%, 22.6% respectively .

Acknowledgement:

The Partial financial support from BRNS Research

Grant No. 2009/36/115-BRNS/ 3371 is gratefully

acknowledged.

Reference:

[1] Ahmad, S. (2003). Techniques for inducing

accelerated corrosion of steel in concrete. Arabian

Journal for Science and Engineering, 34(2), 95–

104.

[2] Ahmad, S. (2009).Reinforcement corrosion in

concrete structures, its monitoring and service life

prediction—a review. Cement and Concrete

Composites, 25(4–5), 459–471.

[3] Cabrera, J.G. (1996). Deterioration of concrete due

to reinforcement steel corrosion. Cement and

Concrete Composites. 18(1), 47–59.

[4] Castro, P., Weva and Balancim, M. (1997).

Corrosion of reinforced concrete in a tropical

marine environment and in accelerated tests.

Construction and Building Materials, 11(2), pp. 75-

88.

[5] Glass, G.K. (2003). Reinforcement corrosion.

Advanced concrete technology set, J. Newman and

B. S. Choo, eds., Butterworth-Heinemann, Oxford,

1–27.

[6] IS 456:2000, Plane and reinforced concrete – code

of practice, Bureau of Indian Standards, New Delhi.

[7] Jung, W. Y., Yoon, Y. S., and Sohn, Y. M. (2003).

Predicting the remaining service life of land

concrete by steel corrosion. Cement and Concrete

Research, 33(5), 663–677.

[8] Law, D.W., Millard, S.G., and Bungey, J.H. (2000).

Linear polarisation resistance measurements using a

potentiostatically controlled guard ring. NDT and E

International, 33, 15–21.

[9] Liang, M. T., Wang, K. L., and Liang, C. H.

(1999). Service life prediction of reinforced

concrete structures. Cement and Concrete Research,

29(9), 1411–1418.

[10] Poupard, O., L’Hostis, V., Catinaud, S., and Petre-

Lazar, I. (2006). Corrosion damage diagnosis of a

reinforced concrete beam after 40 years natural

323 POORNACHANDRA PANDIT, KATTA VENKATARAMANA, K S BABUNARAYAN,




exposure in marine environment. Cement and

Concrete Research, 36(3), 504–520.

[11] Yoon, S., Wang, K., Weiss, W.J., and Shah, S. P.

(2001). Interaction between loading, corrosion,

serviceability Reinforced concrete. ACI Materials

Journal, 97(6), 99-181.

[12] Zhang, W., and Ba, H. (2011). Accelerated life test

of concrete in chloride environment. Journal of

Materials in Civil Engineering, 23(3), 330-334.

Fig1: Reinforcement details of beam specimens

Fig2: Schematic representation of acc accelerated corrosion of beam

Fig3: Beam specimen marked in to number of grid to measure corrosion current density

324 Experimental studies on the effects of corrosion on the flexural strength of

RC beams



Fig4: Testing setup

Fig5: Variation of deflection(mm) with Load(kN) for OPC beam Specimens

Table1: Corrosion current density for OPC beam Specimens

Corrosion current density, icorr (mA/cm

2)

Grid Number 1 2 3 4 5 Avg

Beams

Control Beam 0.0036 0.0049 0.0044 0.0037 0.0045 0.0040

2.5% Corroded Beam 0.0220 0.0231 0.0238 0.0241 0.0246 0.0236

5% Corroded Beam 0.0268 0.0274 0.0282 0.0285 0.0276 0.0277

7.5% Corroded Beam 0.0306 0.0298 0.0308 0.0295 0.0302 0.0302

10% Corroded Beam 0.0268 0.0272 0.0256 0.0264 0.025 0.0262

Table2: Ultimate Loads and Deflection for different rates of corrosion of OPC Beams

Beam Specimen Average of

Ultimate LoadP (kN)

Average of End Beam

Deflection 𝛛 (mm) P/𝛛

P/𝛛, normalized

with respect to

control beams

0% corrosion

(control beams) 92.04 60.96 1.50 1.00

2.5% corrosion 87.83 74.35 1.18 0.79

5% corrosion 86.17 65.77 1.31 0.87

7.5% corrosion 72.14 57.24 1.26 0.84

10% corrosion 63.85 47.13 1.35 0.90

www.cafetinnova.org

Indexed in





ISSN 0974-5904, Volume 07, No. 01

February 2014, P.P.325-330


Modelling of the Cu and Fe transport in sand-bentonite and sand-fly

ash mixtures

SHANKARA1, MAYA NAIK

2 AND P V SIVAPULLAIAH

3

1Dept. of Civil Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bangalore 560035, India,

2Dept. of Civil Engineering, BMS College of Engineering, Bull Temple Road, Bangalore 560019, India,

3Dept. of Civil Engineering, Indian Institute of Science, Sir C.V. Raman Avenue, Bangalore 560012, India,

Email: [email protected] [email protected] [email protected]

Abstract: Evaluation of the various landfill liner systems requires the contaminant transport modeling through

selected liner. Normally clay soils alone or bentonite amended with sand are widely used as bottom liner. Bentonite

is used due to its high adsorption capacity and sand is used to impart the required volume stability and strength. The

performance of the liner has to be assessed for the breakthrough times under different hydrological regime knowing

the hydraulic conductivity of the compacted liner and sorption capacity. The data available on sorption capacity of

the liner for copper and iron is used to select the appropriate sand bentonite mixture combination. It was found that a

mixture of 20% bentonite and 80% sand possess better sorption capacity for copper. The hydraulic conductivity of

this mixture has been determined by laboratory testing. The breakthrough curves, under different hydraulic gradients

for the compacted mixtures and for the diffusion coefficient of copper, are obtained through the use of POLLUTEv7

software for a liner thickness of 1m. To promote the waste materials for liner construction fly ash is often used as

material. To enable comparison and to improve the stability of the 10% fly ash containing 90% sand is used. It was

found that fly ash sand mixture possesses better sorption capacity for iron. But the hydraulic conductivity of the

mixture was high and the break through times as modeled was very small. To reduce the hydraulic conductivity 5%

of bentonite is incorporated to sand fly ash mixture. The breakthrough times as modeled have improved

considerably.

Key words: BTC curves, Pollute, Diffusion, Advective, Contaminant transport, Modelling, Bentonite, Fly ash.

1. Introduction:

The concept of waste containment system is to isolate

the wastes from the surrounding environment, and to

provide an effective leak-proof system. Sharma et al.

[1]. As an integral part of engineered cleanup program,

on-land waste containment systems constructed with

clay materials are common. Natural clay liners have

been extensively used to preclude leakage of effluents

from waste disposal facilities. Christensen et al. [2]. The

suitability of a clay liner has been conventionally

evaluated on the basis of hydraulic conductivity to

control the advective mass transport. From the

consideration lower hydraulic conductivity, strength and

shrinkage the density to be compacted at the water

content required for a given compactive effort has to be

arrived. Generally the liners are compacted at their near

respective optimum moisture content to their maximum

dry densities. Rowe et al [3]. The hydraulic conductivity

of the liner material has been obtained to calculate the

rates of migration of the contaminants through them by

advective transport. However, under optimum

compaction conditions dominant mass transfer in liners

is by diffusion Quigley et al; Rowe 1994 et al [4, 5]. To

calculated the rates of migration of the contaminants by

diffusion process, it is necessary to know the effective

diffusion coefficient of the contaminant in any given

liner material. Further to calculate the contaminant

transport by combined processes of advection and

diffusion it is necessary to know both the velocity of the

fluid through the liner and effective diffusion

coefficients. Daniel et al. [6]. In this paper it is proposed

to determine the rates of migration of copper and iron

ions by pure diffusive transport and through combined

processes of advection and diffusion through sand –

bentonite and sand fly ash compacted liner material. In

most cases the contaminants transport through barrier

system mainly depends on the permeability of the

soil/soil mixture, and advective– diffusion related

phenomena. Rowe et al; Daniel et al. [5, 8]. Also, it is

proposed to study the suitability of using bentonite or

fly ash as liners. To attribute the required strength of the

liner material sand needs to be incorporated in the liner

material. The performance of Sand-Bentonite and Sand-

fly ash mixtures to contain migration of leachate with

respect to typical metal ion contaminants such as copper

and iron has been the main focus of the present study.




326 Modelling of the Cu and Fe transport in sand-bentonite and sand-fly ash mixtures



2. Materials:

2.1. Sand used: A foundry sand which Consists

primarily of clean, uniformly graded, high-quality silica

sand or lake sand that is bonded to form molds for

ferrous (iron and steel) and nonferrous (copper,

aluminum, brass) metal castings. The automotive

industry and its parts suppliers are the major generators

of foundry sand. Silica fine sand was collected from

foundry lab of Amrita school of engineering, Bangalore,

supplied by Arun Alloy Cast Company.

2.2. Fly ash used: A fly ash of class “F” category

procured from Raichur Thermal Power Station (RTPS),

in Karnataka, India, called Raichur fly ash (RFA), used

in the present study. The fly ash used was grey in color;

the physical properties and the chemical composition of

the fly ash are given in Table 1and Table 2. It is seen

that fly ash used contains high amount of sand sized

particles as it is collected from the dump. Though the

fly ash is non-plastic the liquid limit is obtained from

cone penetration is indicative of strength rather than the

plasticity character. The relatively low shrinkage may

be more due its calcium content.

2.3. Bentonite used: Bentonite is a natural clay mineral

and is found in many places of the world it belongs to

2:1 clay family. The basic structure is composed of two

tetrahedrally coordinated sheets of silicon ions

surrounding by a sandwiched octahedrally coordinated

sheet of aluminum ions. The isomorphs substitution of

Al3+ for Si4+ in the tetrahedral layer and Mg2+ or

Zn2+ for Al3+ in the octahedral layer results in a net

negative surface charge on the clay. Compared with

other clay types, it has excellent sorption properties and

possesses sorption sites available within its interlayer

space as well as on the outer edges. Bentonite procured

from Kolar region of Karnataka was used in the present

study and typical analysis is presented in table 3.

2.4. Chemicals used: Synthetic heavy metals were

prepared by dissolving a known quantity of Ammonium

ferrous sulphate in distilled water to represent iron;

similarly cupric sulphate crystals were dissolved in

distilled water to represent copper. pH adjustments were

carried out using 0.1N hydrochloric acid (HCl) and

0.1N sodium hydroxide (NaOH). The chemicals used

were supplied by Qualigens Company of Analytical

Grade (AR).

Table1: Physical Properties of fly ash

Specific gravity 2.03

Liquid Limit (%) 35

Plastic Limit (%) --

Plasticity Index (%) --

Shrinkage Limit (%) 18.5

Compaction Characteristics

Maximum dry density (kN/m3) 11.7

Optimum moisture content (%) 25.0

Grain size distribution

Gravel (%) 00

Sand (%) 58

Silt and clay (%) 42

Table2: Chemical composition of fly ash

Constituents Percentage

SiO2 61.10

Al2O3 28.00

TiO2 1.30

Fe2O3 4.20

MgO 0.80

CaO 1.7

K2O 0.18

Na2O 0.18

L.O.I 1.40

Table3: Physical properties of bentonite

Properties Bentonite

Specific gravity 2.76

Liquid limit, (%) 374

Plastic limit, (%) 63

Plasticity index, % 311

Sediment volume in water (ml/g) 16

Max dry unit weight (kN/m3) 11.7

Optimum moisture content (%) 45

Soil classification (ASTM D24487-

unified Soil classification system CH-Fat clay

Clay fraction, % 2

3. Method:

3.1. Sand bentonite and sand fly ash mixtures selected

for modeling of transport of Cu and Fe ions:

It was shown in the literature Shankara et. al [8] that at

different Soil to Liquid ratios (S/L) sand bentonite

mixture with 20% bentonite sorbs the higher amount of

copper whereas sand fly ash mixture with 10% fly ash

sorbs the higher amount of iron. In this paper these

mixtures are tested for hydraulic conductivity, porosity,

dry density at laboratory conditions and the migration

of respective metal ions are modeled to check their

suitability of breakthrough times in the design of liner

systems. It is found that hydraulic conductivity of the 20

% sand-bentonite and 10% sand-fly ash mixtures are

8.88E-08, 1.78E-05 cm/s respectively. The engineering

327 SHANKARA MAYA NAIK AND P.V. SIVAPULLAIAH



specification for a compacted clay liner usually consists

of a hydraulic conductivity of < 1x10-7

cm/s. In order to

reduce the hydraulic conductivity of 10% sand-fly ash

mixture 5% of bentonite was incorporated to this

mixture. Therefore the mixtures under consideration for

the modelling were i) 20%Bentonite + 80% Sand (Mix

1), ii) 10% fly ash +90% sand (Mix 2) and iii) 10% fly

ash +5% bentonite+85% sand (Mix 3).

3.2. Parameters for contaminant migration model:

3.2.1. Compaction test of soil mixtures:

The compaction tests were conducted using a specially

made apparatus Sridharan et al. [9] which requires about

1/10th

of soil needed for the standard proctor test. Also

the time and effort involved to carry out the compaction

test were less. The sample mold is of 3.81 cm internal

diameter and 4.61 cm external diameter and 10cm in

height. The sample mold assembly has detachable base

plate and a removable collar of 3.50 cm in height. A

hammer of 1kg in weight falls freely through a height of

16cm. The number of blows required to achieve

standard proctor energy per layer is 36 and in three

layers. The remaining procedure is same as that of Light

Compaction test as per IS 2720(part 7) – 1980. A curve

between dry density and water content is plotted as in

Fig.1 and 2. The water content corresponding to

maximum dry density is found from the curve. The

index and Physico chemical properties and hydraulic

conductivities of the soil mixtures are summarized in

Table 4.

Fig1: Compaction Curves for Sand Fly ash mixtures

Fig2: Compaction Curves for Sand Bentonite mixtures

3.2.2. Column Assembly for Hydraulic conductivity:

Hydraulic conductivity and effective porosity values

were estimated independently using standard

geotechnical tests. The column assembly consists of

Plexiglas cylinder of 10 cm long, 4 cm inner diameter

and 0.3 cm thick. The Plexiglas cylinder is attached to

the base plate which houses, a filter paper and a porous

stone. The schematic diagram of hydraulic conductivity

test set up is given in Fig. 3. The bentonite /fly ash after

dry mixing with sand were added with the amounts of

water corresponding to their respective Proctor

maximum dry densities, the wet mixed material was

then compacted using static compaction technique in the

column cell as per ASTM D5856-07 [10]. Prior to the

compacting of the sample in the cell, the inside of the

cell was coated with a thin layer of silicon grease. This

will ensure that there is good contact between the

compacted material and the inner surface of the cell.

Porous discs were placed at top and bottom of the

mould and the mould was tightened. The column cell

with the compacted mix was fitted into position and

connected to the flexible tube as shown in Fig. 3. Water

was allowed to flow through the specimen and the

hydraulic conductivity of the specimen was determined

as per ASTM D5856-07 [10].




Fig3: Schematic diagram of hydraulic conductivity test

set up

1) Graduated water reservior 2) Flexible tube 3)

Column cell 4) Porous stone 5) Sampling flask 6)

Regulating valve 7)‘L’- Length of specimen.

Table4: Geotechnical Properties of the sand bentonite

/fly ash mixtures

Mix Max DD,

(g/cc)

Sp.

Gr

Void

ratio Porosity

H C (k),

cm/s

Mix 1 1.82 2.71 0.490 0.33 8.88E-8

Mix 2 1.72 2.64 0.522 0.34 1.78E-5

Mix 3 1.74 2.65 0.523 0.34 1.18E-7

3.2.3. Input of data into Pollute v7.13 and procedure

to run the software:

Input data into Pollute v7.13 software are tabulated in

Table 5. The input of data were given to the software in

four steps i.e. General data, Layer data, Boundary

conditions and Run parameters. After saving all these

sections POLLUTEv7 program was run and output files

were obtained. The output files can be obtained in excel

and it were imported to origin and the data was plotted.

Table5: Input parameters to POLLUTEv7 software

Input parameter Mix 1 Mix 2 Mix 3

Pure Diffusive Transport mechanism

Darcy’s Velocity,

(cm/s) 0 0 0

Porosity .33 .35 .34

Dry density of soil

mixture, (g/cc) 1.82 1.72 1.74

Migrating ion Cu Fe Fe

Diffusion

Coefficient, (cm2/s) 2.22E-6 2.85E-6 2.85E-6

Constant Source

concentration,

(Mg/L)

100 100 100

Advective Diffusive Transport mechanism, Hydraulic

gradient = 1

Darcy’s

Velocity,(cm/s) 8.88E-8 1.79E-5

1.185E-

7


gradient = 0.5

Darcy’s Velocity,

(cm/s) 4.44E-8 8.93E-6

5.93E-

08


gradient = .33

Darcy’s Velocity,

(cm/s) 2.93E-8 5.89E-6

3.91E-

08


gradient = 0.1

Darcy’s Velocity,

(cm/s) 8.88E-9 1.79E-6

1.19E-

08

4. Results and Discussion:

In this section the variation of relative concentration

with time for copper ion in 20% bentonite + 80% sand

mixture and 10% fly ash +5% bentonite+85% sand,

10% fly ash + 90% sand for iron ion is modelled using

POLLUTEv7 software. The models are prepared by

using the measured hydraulic conductivity, dry density,

porosity and assumed 1 m liner thickness and diffusion

coefficients, it is reported that diffusion coefficients for

copper, iron as 2.22E-6, 2.85E-6 cm2/s respectively. M

Zeki et al. [11]. For the above three mixtures under

different hydraulic gradients contaminant transport

models discussed along with their graphs.

329 SHANKARA MAYA NAIK AND P.V. SIVAPULLAIAH



Fig4: Break through curves for pure diffusive transport condition

Fig. 4 shows the variation of relative concentration with

time for respective ions with pure diffusive transport in

all the three soils. The model clearly shows under pure

diffusion all the three soil mixtures perform better. The

differences in breakthrough times, which are small at

lower relative concentrations, increase quickly and

reach equilibrium at relative concentration of almost 0.6

for 300 years.

Fig5: Break through curves for 10% fly ash +90% sand

mixture under different hydraulic gradients

Fig.5 shows the variation of relative concentration with

time for iron ion with advective diffusive mechanism

the model clearly shows that the 10% fly ash +90% sand

mixture performs very poor as there are breakthrough

times are at lower side under all hydraulic gradients. As

stated above this mixture do not satisfies the hydraulic

conductivity criteria in the liner design, and because of

the higher hydraulic conductivity of the mix the

breakthrough times for all conditions is only <1 year.

Fig6: Break through curves for 20% bentonite+80%

sand mixture under different hydraulic gradients


time for copper ion with advective diffusive mechanism

in soil containing 20% bentonite+80% sand mixture

under different hydraulic gradient conditions. The

differences in breakthrough times, which are small at

lower relative concentrations, increase gradually and

reaches equilibrium at relative concentration of almost

1.0 for hydraulic gradients 1, 0.5 and 0.33, but when

hydraulic gradient is reduced to 0.1, the breakthrough

time quickly increases to 100 years at relative

concentration of 0.6. So this mixture performs better

when hydraulic is lower. Rates of migration of copper

ions in soil containing 20% bentonite+80% sand is

faster this is probably due to higher diffusion coefficient

of iron and lower retardation factor.




Fig7: Break through curves for 10% fly ash +5%

bentonite+85% sand mixture under different hydraulic

gradients


time for iron ion with advective diffusive transport in

soil containing 10% fly ash +5% bentonite+85% sand

mixture under different hydraulic gradient conditions

The differences in breakthrough times, which are small

at lower relative concentrations, increase gradually and

reach equilibrium at relative concentration of almost 1.0

for hydraulic gradients 1,0.5,.33, but when hydraulic

gradient is reduced to 0.1 the breakthrough time quickly

increases to 100 years at relative concentration of 0.6.

So this mixture performs better when hydraulic gradient

is lower. Rates of migration of copper ions in soil

containing 10% fly ash +5% bentonite+85% sand

mixture is faster might be due to higher diffusion

coefficient of iron and lower retardation factor.

5. Conclusion:

Based on the breakthrough curves obtained using

evaluation Pollute v7.13 software for copper through

sand bentonite mixtures for iron for fly ash mixtures for

iron as liners for waste disposal facilities following

conclusions are drawn:

1. Under only diffusion controlled migration both

sand bentonite mixture and san flash mixtures

perform very well as they give breakthrough times

of more than 100 years for both copper and iron.

2. Sand with 20% bentonite mixture performs well to

retard copper only low hydraulic gradient of 0.1

only.

3. Sand with 10% fly ash mixture is poor to retard the

migration of iron even under low hydraulic

gradient.

4. Sand with 10% fly ash and 5% bentonite gives high

breakthrough times for iron and can be used as

liner.

6. Reference:

[1] H. D. Sharma, P. L. Sangeeta, Waste Containment

Systems, Waste Stabilization, and Landfills –

Design and Evaluation, John Wiley and Sons INC,

Canada, 1994.

[2] T. H Christensen, R Cossu, R Stegmann,

Landfilling of Waste: Barriers, E & FN Spon, and

London. 1994.

[3] K. R. Rowe, R.M. Quigley, R.B. Brooker, Clayey

Barrier Systems for Waste Disposal Facilities, E &

FN Spon, London, 1995

[4] R. M. Quigley, F. Fernandez, E K. Yanful,

Helgason, A Margaritis, J. L Whitby, Hydraulic

conductivity of contaminated natural clay directly

below a domestic landfill, Can. Geotech. J., l 24,

377Ð383, 1987.

[5] R.K. ROWE, Diffusive transport of pollutants

through clay liners. In: T.H. Christensen, R. Cossu,

R. Stegmann (eds), Landfilling of Waste: Barriers.

E & FN Spon, London, 219Ð245, 1994.

[6] D.E. Daniel, C.D., Shackelford Diffusion in

saturated soil: I. Background. ASCE J. Geotech.

Eng., 117(3): 467-84, (1991).

[7] Daniel, D.E., Shackelford, C.D., 1988. Disposal

barriers that release contaminants only by

molecular diffusion. Nuclear Chem. Waste

Manage. 8, 299–305.

[8] Shankara, S. N. Maya Naik, sivapulliah, Studies on

Use of Sand-Bentonite and Sand-Fly ash Mixtures

as Prospective Liner Materials to Retain Iron and

Copper in Aqueous Solutions, EMSD, Print: ISSN

2164-7682, Vol. 1, No. 2, 2012.

[9] A. Sridharan and P. V. Sivapullaiah, Mini

Compaction Test Apparatus for Fine Grained Soils,

ASTM Journal of Testing and Evaluation, 28, pp.

240-246, (2005).

[10] ASTM D5856, Standard Test Method for

Measurement of Hydraulic Conductivity of Porous

Material Using a Rigid-Wall, Compaction-Mold

Permeameter. ASTM West Conshohocken, PA,

2007.

[11] M. Zeki Camur, Hasan Yazicigil Impact of Human

Activity on Groundwater Dynamics (Proceedings

of a symposium held during the Sixth IAHS

Scientific Assembly at Maastricht, The

Netherlands, July 2001, IAHS Publ. no. 269, 2001.

www.cafetinnova.org

Indexed in





ISSN 0974-5904, Volume 07, No. 01

February 2014, P.P.331-335


Non Destructive Tests with Rebound Hammer and Ultrasonic Pulse

Velocity Measurements on Geopolymer Concrete

SHANKAR H SANN AND R B KHADIRANAIKAR Department of Civil Engineering, Basaveshwar Engg. College, Bagalkot, Karnataka, INDIA


Abstract: Geopolymer is a class of aluminosilicate binding materials synthesized by thermal activation of solid

aluminosilicate base materials such as fly ash, metakaolin, GGBS etc., with an alkali metal hydroxide and silicate

solution. The geopolymer was activated with sodium hydroxide, sodium silicate and heat. This paper presents the

results of non-destructive testing done on geopolymer concrete. The molarity used for the preparation of geoploymer

specimens was 12. The grades choosen for the investigation were M-30, M-40, M-50 and M-60. The alkaline

solution used for present study is the combination of sodium silicate and sodium hydroxide solution with the ratio of

2.50. The test specimens were 150x150x150 mm cubes heat-cured at 60°C in an oven. The experimental

investigation using NDT methods showed that a good correlation exists between conventional compressive strength,

Schmidt rebound hammer (SRH) and ultrasonic pulse velocity (UPV) on geopolymer concrete, which is similar to

that of conventional concrete. The rebound hammer readings had a correlation coefficient of 0.9144 while the

ultrasonic pulse velocity had a correlation coefficient of 0.8897.

Key words: geopolymer concrete, Schmidt Rebound Hammer (SRH), Ultrasonic Pulse Velocity (UPV), molarity,

sodium hydroxide, sodium silicate.

Introduction:

Geopolymer concrete, named after the reaction between

polymer and geological origin source material proposed

to replace all cement portions in concrete as the main

binder [Davidovits, 1997]. The reduction of cement

portion in concrete is expected to decrease the Portland

cement demand; hence reducing the environmental

issues generated from cement production. Geopolymer

concrete is commonly produced from alkaline liquid

and source material. The alkaline liquid is a

combination of sodium hydroxide or potassium

hydroxide with sodium silicate or potassium silicate

[Barbosa, et al., 2000]. The utilization of single alkaline

hydroxide activator will have lower rate reaction

compared to those containing soluble silicate [Palomo et

al., 1999], therefore sodium silicate solution is added to

sodium hydroxide solution to enhance the reaction rate

between alkaline liquid and source material [van

Deventer, et al., 2000]. Fly ash is the most common

source material for making geopolymers. Normally,

good high-strength geopolymers can be made from class

F fly ash [Schmucker, et al., 2004]. These low calcium

materials were chosen because high calcium content in

source material can affect the polymerization process

[Fenandez-Jimenez, et al., 2003]. The variation of fly

ash and water content was studied in producing the

geopolymer concrete. As fly ash content increases, the

compressive strength also increases as long as there is

sufficient fluid to coat fly ash particles and involve them

in polymerization reaction. This indicates that the fly

ash content directly contributes to compressive strength

of geopolymer concrete. For given water content, the fly

ash content increases the compressive strength of

geopolymer concrete increases up to certain amount

beyond which strength decreases. [Rangan, 2008]

Experimental Investigations:

Materials:

The following materials have been used in the

experimental study [Shankar et al., 2011]

a. Fly Ash (Class F) collected form Raichur Thermal

power plant having specific gravity 2.00.

b. Fine aggregate: Sand confirming to Zone –III of IS:

383-1970 having specific gravity 2.51 and fineness

modulus of 2.70.

c. Coarse aggregate: Crushed granite metal

confirming to IS: 383-1970 having specific gravity

2.70 and fineness modulus of 5.85.

d. Water: Clean Potable water for mixing

e. Alkaline Media: Specific gravity of

i. Sodium Hydroxide (NaOH) = 1.16

ii. Sodium Silicate (Na2SiO3) = 1.57

f. Superplasticizer : Conplast (SP-430)

The tests carried out on the hardened concrete were the

Ultra sonic pulse velocity test, Rebound hammer test

and conventional destructive test. The NDT tests were



332 Non Destructive Tests with Rebound Hammer and Ultrasonic Pulse Velocity

Measurements on Geopolymer Concret


ISSN 0974-5904, Vol. 07, No. 01, February, 2014, pp.331-335

carried out according to IS: 13311-1992 (Part 1 & 2),

whereas the conventional destructive test was carried

out as per IS: 516-1959. The cube specimen of standard

size 150x150x150 mm was considered for the entire

investigation. The age of 7 and 28 days for concrete

cubes were chosen throughout the investigation, since

the UPV and SRH tests were unaffected between 3 days

to 3 months [Mirmiran, 2001].

Mix design of geopolymer concrete:

In the design of geopolymer concrete mix, coarse and

fine aggregates together were taken as 77% of entire

mixture by mass. This value is similar to that used in

OPC concrete in which it will be in the range of 75 to

80% of the entire mixture by mass. Fine aggregate was

taken as 30% of the total aggregates. The density of

geopolymer concrete is taken similar to that of OPC as

2400 kg/m3 [Rangan, 2008]. The details of mix design

and its proportions for different grades of GPC are

given in Table 2.

Mixing, Casting, Compaction and Curing of

Geopolymer Concrete:

Mixing process was divided into two stages, dry mix

and wet mix. Initially, coarse aggregate, fine aggregate,

fly ash was mixed together in rotating pan mixer for 3

minutes. Alkaline activator with the combination of

sodium hydroxide and sodium silicate was prepared just

before the mixing with fly ash. Alkaline solution plays

an important role in geopolymer synthesis for the

dissolution of silica and alumina as well as for the

catalysis of polymerization reaction [Kale, 2007].

Alkaline solutions were then poured into the dry mixed

material and continued for wet mixing for another 4

minutes. The ratio of alkaline liquid to fly ash by mass

was varied accordingly with grade of concrete. The ratio

of Na2SiO3 to NaOH used in the current study was 2.50

for all the mixes. This ratio of 2.50 was selected since it

produced the highest compressive strength [Shankar, et

al., 2012]. The workability of the fresh concrete was

measured by means of conventional slump test. After

casting the specimens, they were kept in rest period for

two days and then they were demoulded. The

demoulded specimens were kept at 60°C for 24 hours in

an oven.

Nondestructive tests on geopolymer concrete:

Nondestructive tests are of great scientific and practical

importance especially the need for quality

characterization of damaged constructions made of

concrete. Its importance can also be seen in the desire

for a proposed change of usage or extension of a

structure, acceptability of a structure for purchase or

insurance, assessment of the quality or integrity of the

repairs, monitoring of strength development in relation

to formwork stripping, curing, pre-stressing or load

application.

This research therefore seeks to compare the most

common non-destructive techniques, the rebound

hammer and the ultrasonic pulse velocity methods so as

to see which method has a superior capability in the

sense that it is capable of providing more information

on geopolymer concrete properties.

Schmidt’s Rebound Hammer Test:

The Rebound Hammer has been around since the late

1940’s and today is a commonly used method for

estimating the compressive strength of in-place

concrete. Developed in 1948 by a Swiss engineer named

Ernst Schmidt, the device measures the hardness of

concrete surfaces using the rebound principle. It is

basically a surface hardness test and is used only on

concrete where the surface has not been carbonated as

the results tend to be very high and unrealistic on a

carbonated surface.

Ultrasonic pulse velocity method:

Ultrasonic pulse velocity of concrete can indicate

degree of dense of the microstructure of concrete, low

porosity and high compactness of the concrete matrix

would lead to higher velocity of propagation of

ultrasonic waves. A complex system of stress waves

develops, which include both longitudinal and shear

waves, and propagates through the concrete. The first

waves to reach the receiving transducer are the

longitudinal waves, which are converted into an

electrical signal by a second transducer. Electronic

timing circuits enable the transit time T of the pulse to

be measured.


Workability of geopolymer concrete:

Fresh GPC mixes were found to be highly viscous and

cohesive with medium to high slump. The workability

of the geopolymer concrete decreases with increase in

the grade of the concrete as presented in Table 1, this is

because of the decrease in the ratio of water to

geopolymer solids. Hence we can say that as the grade

of the concrete increases, the mix becomes stiffer

decreasing the workability.

Correlation of compressive strength between

rebound hammer and destructive testing:

A correlation of compressive strength between RSH and

destructive testing for geopolymer concrete for 7 and 28

days are given in Fig. 1 and Table 3. It shows that

compressive strength by SRH is higher than destructive

test results for the samples. As SRH is based upon

surface hardness, the compressive strength becomes

higher in all the cases. SRH test results give a

333 SHANKAR H SANN AND R B KHADIRANAIKAR



conservative value. Regression analysis was computed

on the data obtained; the rebound hammer readings

had a correlation coefficient of 0.9144.

The regression equation for the rebound hammer

method is s = 0.928 r + 5.067.

Correlation of compressive strength between

ultrasonic pulse velocity and destructive testing:

In case of Portland cement based concretes, an UPV

value of more than 4 km/sec represents a very good

quality quality [IS:13311]. As the chemical nature of

matrix of P-C based concretes and GPCs are different, a

direct comparison of UPVs in these type concrete is not

rational. However the observed UPV for different

grades of GPC are in the range of 3.57 to 5.4 km/sec

which indicate that the concrete is ‘Good’ to ‘Very

Good’ type.

A correlation of compressive strength between UPV and

destructive testing for geopolymer concrete for 7 and 28

days are given in Fig. 2 and Table 3. It shows that

compressive strength by UPV is slightly deviating from

the destructive test results. Regression analysis was

computed on the data obtained; the ultrasonic pulse

velocity had a correlation coefficient of 0.8897. The

regression equation for the UPV is s = 0.0582 v +

1.6807. Where s is the strength and r is rebound

number, v is the ultrasonic pulse velocity.

Correlation between rebound hammer and ultra-

sonic pulse velocity readings:

Although SRH gives the compressive strength but UPV

helps to determine the density, uniformity and

modulus of elasticity of the concrete structures which

are the factors for durability of the structures and

also predicting the service life of the structures but

compressive strength is one of the parameter which

always has a prime importance for determining the

quality of the structure [Mohammadreza Hamidian, et

al., 2012]. As SRH is very much handy for determining

the compressive strength, a correlation with UPV will

be very much helpful for establishing the

standardization of both NDT methods for better

accuracy. A correlation showed in Fig. 3 between

compressive strength by SRH and UPV where a best

fitted curve is drawn to show the relation between these

two values. Regression analysis was computed on the

data obtained; the compressive strength by SRH had a

correlation coefficient of 0.8716. The regression

equation is s = 15.566 r – 20.057.

Conclusions:

Based on the obtained results of the present

investigations the following conclusions can be drawn:

1. The fly ash can be used to produce geopolymeric

binder phase which can bind the aggregate systems

consisting of sand and coarse aggregate to form

geopolymer concrete (GPC).

2. Conventional methods of mixing, compaction,

moulding and demoulding was adopted for GPC’S

also.

3. The workability of freshly prepared geopolymer

concrete mix decreases with increase in grade of

concrete.

4. The routine techniques were employed for

conducting NDT tests on geopolymer concrete,

which were similar to conventional concrete

testing. The experimental investigation showed that

a good correlation exists between compressive

strength, Schmidt Rebound Hammer and ultrasonic

pulse velocity for a geopolymer concrete.

5. The sensitivity of the pulse velocity test in

measuring strength is affected by the concrete age,

as the concrete matures, the sensitivity of the

ultrasonic pulse velocity to strength achieved by the

geopolymer concrete increases.

6. The rebound hammer shows less sensitivity as the

concrete matures since it is a surface hardness test

and for age above 7 days there is little or no gain in

surface hardness.

7. The regression equation for the rebound hammer

method is s = 0.928 r + 5.067, whereas that of

ultrasonic pulse velocity method is s = 0.00582 v +

1.6807 with destructive testing and the combined

correlation of SRH and UPV is s = 15.566 r –

20.057 on geopolymer concrete.

y = 0.928x + 5.067

R2 = 0.9144

0

10

20

30

40

50

60

70

0 20 40 60 80

Destructive Comp. Strength

Reb

ou

nd

Ham

mer

Fig1: Correlation b/n SRH Vs Comp. strength

334 Non Destructive Tests with Rebound Hammer and Ultrasonic Pulse Velocity

Measurements on Geopolymer Concret



y = 0.0582x + 1.6807R² = 0.8897

0

1

2

3

4

5

6

0 20 40 60 80Ult

raso

nic

pu

lse

ve

loc

ity

Destructive Comp. strength

Fig2: Correlation b/n UPV Vs Comp. strength

y = 15.566x - 20.057R² = 0.8716

0

10

20

30

40

50

60

70

2 3 4 5 6Reb

ou

nd

Ham

mer

Ultrasonic Pulse Velocity

Fig3: Correlation b/n SRH Vs UPV

Acknowledgements:

The authors are thankful to the reviewer Dr. N. P.

Rajamane, SRM University for his valuable and critical

suggestions that helped to enhance the quality of the

paper. The authors are also thankful to Prof. D. Venkat

Reddy, Editor-in-Chief of IJEE, Dr. M. C. Narasimhan,

Professor, NITK Surathkal and Dr. B. T. Patil, former

Principal, GMIT, Davanagere for their valuable

suggestions.

Reference:

[1] Davidovits, J., High Alkali Cements for 21st

Century Concretes, Concrete Technology: Past,

Present and Future. P. K. Mehta, ACI, Detroit,

USA. 1997, SP 144-19:383-397.

[2] Barbosa, V.F.F., MacKenzie, K. J. D. et al,

Synthesis and Characterization of Materials Based

on Inorganic Polymers of Alumina and Silica:

Sodium Polysialate Polymers, International Journal

of Inorganic Materials 2(4), 2000, 309-317.

[3] Palomo, A, Grutzeck, M. W. et al, Alkali-

Activated Fly Ashes, A Cement for the Future,

Cement and Concrete Research, 29(8), 1999, 1323-

1329.

[4] Xu, H. and van Deventer, J.S.J. The

Geopolymerisation of Alumino-Silicate Minerals,

International Journal of Mineral Processing, 59(3),

2000, 247-266.

[5] Schmucker, M. and MacKenzine, K. J. D

Microstructure of sodium polysialate

siloxogeopolymer, Ceramic International, 2004,

433-437.

[6] Fenandez-Jimenez, A and Palomo, A.,

Characteristics of fly ashes, Potential reactivity as

alkaline cements, Fuel, 2003, 2259-2265.

[7] Rangan, B.V., Mix design and production of flyash

based geopolymer concrete, The Indian Concrete

Journal, 82(5), 2008, 7-14.

[8] Mirmiran, A. and Wei, Y., Damage assessment of

FRP-encased concrete using ultrasonic pulse

velocity. J. Eng. Mech., 127, 2001, 126-135.

[9] Kale, D. and Chaudary, R., Mechanism of

geopolymerization and factors influencing its

development: A review, Journal of Material

Science, 42(3), 2007, 729-746.

[10] Mohammadreza Hamidian, Ali Shariati, M. M. et

al, Application of Schmidt rebound hammer and

ultrasonic pulse velocity techniques for structural

health Monitoring Scientific Research and Essays,

7(21), 2012, 1997-2001.

[11] Shankar H. Sanni and R. B. Khadiranaikar,

Performance of geopolymer concrete under various

severe environmental conditions, International

Journal of Civil and Structural Engineering, 3(2),

2012, 396-407.

[12] Shankar H. Sanni and et al., Permeability

characteristics of geopolymer concrete, B.E Project

Report, Basaveshwar Engineering College,

Bagalkot, 2011.

[13] M. S. Shetty, Concrete Technology, (S. Chand and

Company Ltd., New Delhi, 2002)

[14] IS: 2386 (Part-IV)-1963, Methods of test for

aggregates for concrete-mechanical properties,

Bureau of Indian standards, New Delhi.

[15] IS: 456-2000, Code of practice for plain and

reinforced concrete, Bureau of Indian standards,

New Delhi.

[16] IS: 383-1970, Specification for coarse and fine

aggregates from natural sources for concrete,

Bureau of Indian standards, New Delhi.

[17] IS: 516-1959, Methods of test for strength of

concrete, Bureau of Indian standards, New Delhi.

[18] IS: 13311(PT1): 1992, Methods of non-destructive

testing of concrete: Part 1 Ultrasonic pulse velocity,

Bureau of Indian standard, New Delhi.

[19] IS: 13311(PT2): 1992, Methods of non-destructive

testing of concrete: Part 2 Rebound hammer,

Bureau of Indian standard, New Delhi.

335 SHANKAR H SANN AND R B KHADIRANAIKAR



Table1: Slump values for different grades of GPC

Grade Na2SiO3/ NaOH Slump (mm)

M-30 2.5 135

M-40 2.5 130

M-50 2.5 110

M-60 2.5 95

Table2: Mix proportions of GPC mix with molarity of 12M (Na2SiO3/ NaOH as 2.5)

Materials Mass (kg/m

3)

M-30 M-40 M-50 M-60

Coarse

aggregates

20 mm 277.20 277.20 277.20 277.20

14 mm 369.60 369.60 369.60 369.60

7 mm 646.80 646.80 646.80 646.80

Fine sand 554.40 554.40 554.40 554.40

Fly ash 380.69 394.29 408.89 424.62

Na2SiO3/ NaOH 2.50 2.50 2.50 2.50

SiO2/Na2O (by mass) 2.00 2.00 2.00 2.00

Sodium hydroxide solution 48.95 45.06 40.89 36.4

Sodium silicate solution 122.36 112.65 102.22 91

Super Plasticizer 5.70 5.91 6.13 6.37

Extra water 38.06 39.42 40.88 42.46

Table3: Compressive Strength for different grades

Sl. No Grade Rebound Hammer (MPa) UPV (km/sec) Destructive test

7 Days 28 days 7 Days 28 days 7 Days 28 days

1 M 30 28.4 35 3.57 3.75 25 32

2 M 40 36.8 44 3.48 3.85 38 40

3 M 50 47 56 4.05 4.67 44.6 58

4 M 60 54 62 4.87 5.4 56 54

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ISSN 0974-5904, Volume 07, No. 01

February 2014, P.P.336-340


Performance Studies on Cement Stabilized Gravel Soils Exposed to

Acid Environment

A C S V PRASAD AND C N V SATYANARAYANA REDDY Department of Civil Engineering, Andhra University, Visakhapatnam-533003, INDIA


Abstract: Earth is being used as construction material in all parts of the world for all civil engineering works. Some

locally available soils are not suitable for intended purpose, due to lack of properties. Stabilization of soils in order

to improve strength and durability properties often relies on additives such as cement, lime, fly ash, and other

chemicals. These materials are low-priced, relatively easy to apply and provide benefits to different soils. In the

present study, comprehensive laboratory work is carried out on the durability characteristics of cement stabilized

gravelly soils exposed to three hydrochloric acid solutions with concentrations of 1%, 3% and 5%, with exposure

periods up to 6months. Two types of soils, namely clayey gravel (GC) and silty gravel (GM) are stabilized with

varying cement content ranging from 4 to 14% by dry weight of the soil in increments of 2%. As per the results, the

compressive strength gain of CSGM and CSGC cubes with age continued with addition of cement under the same

concentration of HCl acid. However, the compressive strength decreased with increasing concentration of

hydrochloric acid for same cement content.

Keywords: Cement stabilization, compressive strength, durability, hydrochloric acid.

1. Introduction:

The construction cost in many projects can be

substantially reduced by the use of stabilized local soils

in place of conventional concrete materials.

Encountering difficult and problematic soil seems to be

unavoidable in some construction projects due to

various reasons in many places. Therefore, it is

necessary to search the suitable solution to improve the

properties soil. Soil stabilization is one of the techniques

for improving the properties of poor soils. The

engineering properties of soils and gravels, such as

plasticity and strength can often be improved

significantly by mechanical stabilization, cement

stabilization, lime stabilization and addition of chemical

stabilizing agents. Generally cement stabilization is

used for granular and sand soils for improving the

strength and durability of the soils. Cement stabilization

is generally recommended for construction of roads

(Ingles and Metcalf [1]). The major engineering benefits

of cement stabilization are increased strength, stiffness,

better volume stability and increased durability. HCl is

not a common natural chemical compound, but it can

cause damage to concrete in industrial environments.

Many factors, such as cement type, HCl concentration

and exposure period may affect the acid resistance.

1.1. Literature Review:

The acidic attack is based on the interaction of the

environment and cement based materials, both being of

complex character. The rate of the attack may be

influenced, i.e. accelerated or inhibited by many factors.

All of them should take into consideration the

evaluation of the aggressiveness of the medium and the

resistance of cement based materials and the choice of

protective measurements.

Lohani et al [2] observed that compressive strength of

concrete with quarry dust as partial replacement to sand,

increased with dust content up to 30% and thereafter

decreased. Further it was reported that the compressive

strength of quarry dust concrete continued to increase

with age for all the percentage of quarry dust contents.

Based on durability studies, it was reported that there

was no loss of strength for immersion in

Magnesium-sulfate (MgSO4) and Sodium-chloride

(NaCl) solutions in comparison with immersion in

normal water and the strength gain continued in

almost all specimens with no loss in weight.

However, in case of hydrochloric acid (HCl) solution, it

was observed that there was a loss of strength and

weight in comparison with immersion in normal water.

The deteriorating effect was observed to increase with

increase in time of exposure of concrete to HCl solution.

Arunakanthi et al [3] investigated on high-performance

concrete (HPC) with partial replacement of cement by

20% metakaolin and subjected to various concentrations

of HCl. Compressive strength and split tensile strength

of HPC increased with the partial replacement of



337 A C S V PRASAD AND C N V SATYANARAYANA REDDY



cement by 20% metakaolin. However, the strengths

decreased with the increase in concentration of HCl in

mixes and curing water.

Madhusudhana Reddy et al [4] investigated that the

effect of Hydrochloric acid (HCl) on Blended Cement

[Fly ash based (BC)] and Silica Fume Blended Cement

(SFBC) and their concretes. The results showed that

with increase in HCl concentration there is retardation

in initial and final setting of cements (BC and SFBC).

The compressive strength of both blended cement

concrete and silica fume blended cement concrete has

reduced with an increase in the concentration of HCl at

both 28 and 90 days. Compressive strengths of BCC and

SFBCC have decreased in the range of 2 to 19%, at 28

and 90 day age respectively, with an increase in HCl

concentration (100mg/l to 900mg/l), when compared

with the control specimens.

1.2. Materials and Experimental Program:

1.2.1. Gravel Soil:

The soils investigated in the present study are

procured from gravel quarries located near

Tadepalligudem in West Godavari District, Andhra

Pradesh. The engineering properties of soils are

determined from laboratory investigations as per IS

2720 [5, 6, 7] and are presented in Table 1. The effect

of compaction on grain size of the soils has been also

studied. The grain size distribution curves of soils

before & after IS heavy compaction tests are shown in

Fig.1.

Fig.1 Gradation curves for Gravel Soils

0

20

40

60

80

100

120

0.01 0.1 1 10 100

Particle size in mm

% f

iner

,N

GC-before compactionGC- after compactionGM-before compactionGM-after compaction

Table1: Engineering Properties of Gravel soils

Soil property Soil 1 Soil 2

Grain size analysis

Before compaction

Gravel size (%)

Sand size (%)

Fines (%)

61

22

17

76

15

9

After compaction

Gravel size (%)

Sand size (%)

Fines (%)

56

23

21

40

36

24

Plasticity Characteristics

Liquid limit (%)

Plastic limit (%)

Plasticity Index (%)

39.7

16

23.7

33.2

23.6

9.6

IS classification of soil GC GM

Compaction characteristics

Optimum moisture content (%)

Maximum dry density (g/cc)

8.0

2.15

12.4

1.91

Soaked C.B.R (%) 19.5 21.9

Differential free swell index (%) 18.2 0

1.2.2. Cement:

The cement used in the study is 43 grade Ordinary

Portland Cement. The properties of cement

determined from laboratory tests are presented in

Table 2.

Table2: Properties of Cement

Property Value

Specific Gravity 3.12

Initial setting time (min) 165

Final setting time (min) 230

Compressive strength (N/mm2)

i) at 3 Days 31

ii) at 7 Days 42

1.3. Details of Experimental Studies:

1.3.1. Compaction tests:

The compaction characteristics are determined by

performing heavy compaction tests on gravel soil

mixed with varying percentages (0-14) of cement by

weight. The tests are performed as per IS 4332 (part

3)-1995 [8].

1.3.2. Preparation of specimens:

Cement stabilized soil cube specimens of size 150mm

x 150mm x 150mm are cast from gravel soil

stabilized with varying percentages of cement by

compacting at respective OMC and MDD values. The

densities may vary ± 0.01.The selected soil materials

are first thoroughly mixed with the cement and then

distilled water is added and again mixed till uniform

consistency is obtained. The prepared mix is poured

into the cube moulds in three layers and are

compacted with a tamping rod and vibrated on

vibrating table to remove the entrapped air. After

casting and finishing, the moulds are covered with

plastic sheets and kept under laboratory conditions for

338 Performance Studies on Cement Stabilized Gravel Soils Exposed to Acid Environment



24 hours and then demoulded. After demoulding, the

specimens are cured by covering with wet gunny bags

at room temperature for further 27 days.

1.3.3. Exposure and testing:

After 28 days of curing, the cement stabilized soil

specimens are placed in tubs containing the following

chemical solutions.

Hydrochloric acid concentrations

i. 1% HCl (10ml/l)

ii. 3% HCl (30ml/l)

iii. 5% HCl (50ml/l)

The exposure solutions are prepared by mixing

Hydrochloric acid with distilled water. Nine

specimens representing similar composition are

immersed in each solution. The concentrations of the

HCl acid solution are checked periodically and the

solution is changed after three months period.

Three cement stabilized soil cube specimens

representing similar composition are retrieved from

the test solution after 1, 3 and 6 months of exposure.

The effect of hydrochloric acid on cement stabilized

soil is evaluated by measuring the compressive

strength.

1.4. Results and discussion:

1.4.1. Compaction characteristics:

The results of IS heavy compaction tests on gravel

soils and cement stabilized gravel soils are presented

in Table 3. It can be observed that from the test results

that the maximum dry density of gravel soils

increased slightly with increase in cement content

initially and thereafter decreased slightly and after

that the change became insignificant. The OMC

values increased slightly with increased cement

content.

1.4.2. Compressive Strength:

The strength development trend of cement stabilized

clayey gravel soil (CSGC) is depicted in Figures 2 to

4 and cement stabilized silty gravel soil (CSGM) is

shown in Figures 5 to 7. The results of CSGC

exposed to 1% HCl acid solution presented in Fig. 2

show that the compressive strength of cement

stabilized cubes for different exposure periods

increased with increase in percentage of cement

content up to 12% and thereafter the compressive

strength decreased with increase in the exposure

period due to deterioration of the hardened cement

with acid attack. The same trend is observed in the

other exposure solutions with HCl acid concentrations

of 3% and 5% (Figs 3 and 4). However, the

compressive strength of CSGC exposed to HCl acid

decreased with increasing concentration of HCl acid

at same exposure period for all cement contents.

Figures 5 to 7 shows the results for the CSGM

exposed to 1%, 3% and 5% HCl acid solutions. It may

be noticed from the figures that the compressive

strength of cement stabilized cubes for different

exposure periods increased with increasing

percentage of cement content. However, the

compressive strength of CSGM exposed to HCl acid

decreased with increasing concentration of HCl acid

at same exposure period for all cement contents.

Table3: Compaction characteristics of cement

stabilized gravel soils

Details of Mix

Compaction characteristics of

soils

GC GM

OMC

(%)

MDD

g/cc

OMC

(%)

MDD

g/cc

Soil + 0% cement 8 2.15 12.4 1.91

Soil + 2% cement 8.1 2.17 12.5 1.96

Soil + 4% cement 8.3 2.18 12.52 1.96

Soil + 6% cement 8.6 2.18 12.58 1.97

Soil + 8% cement 8.8 2.19 12.65 1.95

Soil + 10% cement 9 2.19 12.72 1.95

Soil + 12% cement 9.6 2.2 12.8 1.96

Soil + 14% cement 10 2.2 13.1 1.97

339 A C S V PRASAD AND C N V SATYANARAYANA REDDY



1.5. Conclusions:

Based on the studies carried out on cement stabilized

gravel soils exposed to different concentrations of

hydrochloric acid solutions for different exposure

periods, the following conclusions are made.

The compressive strength of Cement Stabilized

Clayey Gravel (CSGC) under study is not affected

by HCl acid for stabilizing cement contents below

12 percent.


Clayey Gravel (CSGC) reduced when cement

content used for stabilization is above 12%.


Silty Gravel (CSGM) under study is not affected by

HCl acid attack for stabilizing cement contents up

to 14 percent.

However, the rate of gain in compressive strength

of both CSGC and CSGM slowed down with age

for all cement contents.

The compressive strengths of CSGC and CSGM

under study decreased with increasing

concentration of HCl acid.

340 Performance Studies on Cement Stabilized Gravel Soils Exposed to Acid Environment



The Cement Stabilized Clayey Gravel has better

resistance to HCl acid attack compared to Cement

Stabilized Silty Gravel.

The effect of HCl acid on cement stabilized soils is

less significant compared to hydrochloric acid

attack on concrete as amount of cement used for

stabilization is not high.

1.6. Acknowledgement:

The authors thank Prof. E. Saibaba Reddy and Prof. S.

Nagendra Prasad, the reviewers for sparing their

valuable time for reviewing the research paper and for

their valuable suggestions. The authors also thank Prof.

D. Venkata Reddy, Editor- in- Chief, IJEE, for

extending necessary help in publication of the paper in

the journal.

1.7. Reference:

[1] O.G. Ingles, and J.B. Metcalf, “Soil Stabilization –

Principles and practice”, Butterworth’s, Australia,

(1972).

[2] T. K. Lohani, M. Padhi, K.P. Dash and S. Jena,

“Optimum Utilization of Quarry Dust as Partial

Replacement of Sand in Concrete”, International

Journal of Applied Sciences and Engineering

Reseach, (2012), Vol. 1, No. 2, pp. 391-404.

[3] E. Arunakanthi, H. Sudarsana Rao and I.V. Ramana

Reddy, “Effects of Hydrochloric Acid in Mixing

and Curing Water on Strength of High-

Performance Metakaolin Concrete”, International

Journal of Applied Engineering and Technology,

(2012), Vol. 2, No.2, pp.68-76.

[4] B. Madhusudhana Reddy, H. Sudarsana Rao and

M.P. George, “Effect of Hydrochloric Acid (HCl)

on Blended Cement (Fly Ash based) and Silica

Fume Blended Cement and their Concretes”,

International Journal of Science and Technology

(2012), Vol. 1, No. 9, pp. 476-480.

[5] IS: 2720 Part 4, Indian Standard Code of Practice-

Methods of Tests for soils- Grain Size Analysis,

(1985).


Methods of Tests for soils- Determination of Free

Swell, (1977).


Methods of Tests for soils- Determination of Liquid

and Plastic limit, (1985).


Methods of Tests for Stabilized soils, (1995).

www.cafetinnova.org

Indexed in





ISSN 0974-5904, Volume 07, No. 01

February 2014, P.P.341-348


Structural Characteristics of Laterite Blocks

GANESHA MOGAVEERA AND G SARANGAPANI Dept. of Civil Engineering, National Institute of Engineering, Mysore-570 006, India, Visvesvaraya Technological

University, Belgaum, Karnataka, INDIA


Abstract: This paper reports the experimentally obtained values of compressive strength, water absorption and

chemical composition of laterite blocks collected from four different quarries. In this investigation an attempt has

also been made to determine the stress-strain characteristics and water transport phenomenon of one of the types of

laterite blocks. Three types of cement mortars i.e. 1:3 cement mortar; 1:4 cement mortar and 1:6 cement mortar have

been considered in this investigation for the study of water transport phenomenon. From the studies, the wet

compressive strength of laterite blocks is found to vary from 0.5 MPa to 1.9 MPa. The wet strength is around 33% to

74% of dry strength. It is also observed that, the compressive strength increases as the iron content increases.

Key words: Laterite block, Sand, Compressive strength, Chemical analysis, Water transport, Modulus of elasticity.

1. Introduction:

Laterite rocks have been used for building construction

in tropical and subtropical regions of the world where

they are readily available and economical compared to

other natural stones. Laterite from the western coastal

region of India has been utilised for the construction of

historic monuments like ports, palaces, temples,

churches and residential structures. They are mainly

used as building blocks for construction of masonry in

buildings [IS 3620- 1979].In India, laterites occurs in

the states of Goa, Kerala, Karnataka, Maharashtra,

Tamil Nadu, Andhra Pradesh, Bihar, Assam, Meghalaya

and Orissa. Plate 1 to 3 shows some of buildings where

laterite blocks have been used for construction.

Plate1: Building in Goa

Plate2: Jade Hills Homestay, Coorg

Plate3: Shenbagha Vilasan heritage homestay

Laterite is one of the types of masonary units which are

widely used in the present day constructions as they are

cost effective, energy efficient and environmental


342 Structural Characteristics of Laterite Blocks



friendly building material. The word “laterite” has been

derived from the Latin word “later” meaning brick

[Gidigasu M.D (1974)]. Laterite is an unusual soil

which is rich in iron and alumina. They are usually

found in heavily rainfall areas all over the world. They

get formed by intensive and long-lasting weathering.

Silica in the clay is usually leached out over a long

period of time leaving a soil rich in iron oxides,

hydroxides and alumina. When such lateritic soils are

exposed to atmosphere, the iron hydroxides lose the

moisture quickly to form iron oxides, which develop a

good bond with other particles in soil to form the laterite

blocks. Laterite cannot be placed in the triplet family of

rocks, namely Igneous, Sedimentary or Metamorphic. It

may be considered to be a metasomatic rock [Kasthurba

et. al 2007)]. Metasomatism is a metamorphic process

by which the chemical composition of a rock or rock

portion is altered in a pervasive manner which involves

the introduction and/or removal of chemical

components as a result of the interaction of the rock

with aqueous fluids (solutions). During metasomatism,

the rock remains in a solid state [Zharikov, et.al (2007)].

There is a wide variation in the property and appearance

(color, texture & structure) of laterite blocks. As such it

is very difficult for the engineer’s to identify and select

laterite block for building purposes. Further testing of

laterite blocks is cumbersome due to laborious specimen

preparations.

In this experimental investigation, compressive strength,

water absorption and chemical composition of laterite

blocks collected from four different quarries have been

determined. Detailed water transport studies and stress-

strain behavior has also been done for one of the types

of laterite blocks. Laterite blocks of size 325mm x

220mm x175mm have been used in this study.

Literature Review:

Very few investigations have been done on laterite

blocks and laterite block masonry, eventhough it is

being used in many Civil Engineering structures.

Kasthurba et.al (2005-b) carried out a detailed study of

laterite building stones from four major quarries in

widely scattered locations of Malabar region, Kerala.

The compressive strength of laterite blocks were

evaluated according to Indian standard specifications.

According to this study, the strength of laterites depends

on the specimen size and its geometry. It has been

observed that decrease in the size of cube specimens is

accompanied by increase in compressive strength, as in

concrete cubes. In the reported results, compressive

strength of most of the specimens tested were below 3.5

MPa, which is the prescribed minimum for use in

laterite stone masonry, as per IS 3620-1979. Since the

local practitioners vouch for the good quality of laterite

from these local quarries, this study has suggested a

relook into the codal provisions. It has also been

suggested that the strength evaluation of laterite be

carried out on standard size blocks used for masonry,

like in the case of bricks and hollow blocks, instead of

cubes.

Kasthurba et.al (2006-a) evaluated laterites based on

their performance in traditional buildings and also by

determining engineering properties of fresh laterite from

widely located quarries within Malabar region, Kerala.

There is a wide variation in the experimental results

(1.3- 4.3 MPa) of compressive strength of commercially

available, machine-cut laterites from Malabar region.

From a comparison of wet and dry strengths, it is

observed that there is a significant reduction in strength

(47-75%) due to saturation. Hence, it is suggested that

laterite masonry is to be protected from dampness.

Kasthurba et.al (2006-b) studied the weathering forms

and properties of laterite building stones used in historic

monuments of Western India. This study found that the

deterioration of laterite masonry may be caused due to a

variety of reasons. They have identified dampness as a

major factor which induces deterioration and hence

protection from dampness would prolong the life of

laterite monuments.

Kasthurba et. al (2007) investigated laterite stones used

for building purpose from Malabar region of Kerala

state in India. According to this investigation, laterites

show a wide variation in their engineering properties

depending on the geographic location of the quarry and

within a quarry with depth. It is noted that specific

gravity and compressive strength decreases with depth

whereas water absorption increases with depth, which

results in a decline in quality of laterite blocks of the

deeper layers. According to the authors, laterites with

dark reddish brown to red colour, taken from top portion

of the profile, generally possess better strength, higher

specific gravity and lower water absorption and hence

are good for building purposes.

Sujatha et.al (2008) carried out tests on laterite blocks

and determined the secant modulus at 30% of ultimate

stress. The secant modulus of the laterite blocks tested

varied from 749MPa to 1240MPa.The compressive

strengths of commercially available laterite blocks of

Mangalore region varies from 1.8 to 4.83MPa. This has

been reported by Sujatha et.al (2008a). They have also

studied the effect of size of laterite blocks on the

compressive strength. As the size of the block decreases

the strength of the block increases.

Gidigasu et.al (1974) has discussed extensively the

chemistry and pedology of laterite blocks. The strength

of laterite blocks is generally low. An earlier study by

concrete and soil research laboratory, Chennai showed

that 150mm cube made out of Kozhikode laterite gave

343 GANESHA MOGAVEERA AND G SARANGAPANI



strength of 1.7MPa.A more recent study of laterite from

Dakshina kannada district by Arun kumar Bhat et.al

(1997) gave a strength ranging from 1.04MPa to

3.47MPa.There was a wide variation in strength from

place to place. Of the 13 locations studied, 11 locations

had strengths below 2.5MPa.It must however be pointed

out that laterite has been used for a long time for house

construction when the height is limited to two stories.

There is a need to select the laterite of reasonable

strength to meet the requirements of two storied

buildings.

BIS code of practice for laterite blocks specifies an

average strength of 3.5 MPa. The standard laterite block

is supposed to have a thickness of 190 mm.The

minimum strength for burnt brick of 75 mm thickness is

also 3.5MPa. It is unrealistic to expect strength of

3.5MPa for a block of 190 mm thickness. The masonry

efficiency of laterite block masonry vis-a-vis burnt brick

masonry is bound to be better due to reduction in the

number of horizontal joints. The minimum strength

specification could probably be brought down to value

around 2 to 2.5MPa to take these factors into account.

Many studies have been made on the other different

types masonry units such as clay bricks, stabilised mud

blocks, boulder blocks, concrete blocks etc. Most of

the studies made by several investigators are on the

different characteristics such as compressive strength,

stress-strain behavior, water transport phenomena etc.

Sarangapani et.al (1998) studied the different

characteristics of four different types of bricks that are

available in and around the Bangalore region.

They also observed the rapid absorption capacity of

bricks in the initial stages of soaking. They found that

the bricks to attain 75% saturation water content, if they

are soaked in water for 20 minutes. The rate of

absorption slows down to a very low value after 75%

saturation. Further they also carried out studies on water

transport from mortar to brick and recommended the use

of partially saturated bricks in the masonry construction.

Suresh Chandra (2012) in his elaborate studies on

various types of masonry units determined the

properties such as compressive strength, water

absorption, flexural strength, initial rate of absorption

and stress – strain characteristics of stabilized mud

blocks, hollow concrete block bricks, hand molded solid

concrete blocks, waste plastic concrete blocks, boulder

blocks and stones. He also studied the water transport

phenomena of stabilised mud blocks, boulder blocks,

hollow concrete blocks and bricks.

2. Experimental Program:

The information available on the properties of laterite

blocks are scanty, as such this experimental

investigation has been taken up. In this experimental

study the program consists of determining the

compressive strength, water absorption and chemical

composition of laterite blocks collected from four

different locations. The stress-strain characteristics and

water transport studies have also been made for one of

the types of laterite which had the highest compressive

strength compared to the other types of laterite blocks.

Table 1 gives the details of the sources from where the

laterite blocks were collected for the test.

Table1: Details of sources of laterite blocks

Source Designation

Balnad Type I

Churipadavu Type II

Sulliapadavu Type III

Peruvie Type IV

All the four sources are in the Mangalore region of

western India. They are located between 74⁰30′ E to 70○

E longitude and 10○N to 12

○ 30′N latitude. Large

volume of mining is done in all the four quarries from

where the laterite samples have been collected. Plate 4

shows the location of the selected quarries. Samples

were collected at the depth of 1.0 m in all the quarries.

Plate4: Laterite samples

http://www.google.co.in/imgres?imgurl=http://s1.wdstatic.com/images/it/ll/d/d2/Laterite_quarry,_Angadipuram,_India._C_004.jpg&imgrefurl=http://www.wikideep.it/suolo/&usg=__vxQaEsX7T-ThO7DZ7a02ZXk53N0=&h=324&w=210&sz=30&hl=en&start=10&sig2=i9nOfJ-jO_np71I8L1HQAQ&zoom=1&tbnid=Mj2JbMxOzQW7GM:&tbnh=118&tbnw=76&ei=0NPLTb78FoiGvAOR3KDtBQ&prev=/search?q=images+of+laterite+quarry&hl=en&sa=X&biw=1366&bih=585&tbm=isch&prmd=ivns&itbs=1




The following sections give the details of tests

conducted.

2.1. Chemical Composition:

The properties of the laterite depend on its chemical

composition. De creteset (1938) tried to classify the

laterite based on the chemical composition. The colour

and texture of the laterite block is a function of the

chemicals that are present in the block. This has been

shown by Eastagonal and Shange-Tu (1940) in the

studies made on the laterite blocks of china.

In this study a detailed chemical analysis has been done

to determine the chemical composition of all the four

types of laterite blocks that have selected for the study.

The procedure given in IS: 2720 (Part XXV) – 1982 has

been adopted to determine the chemical composition of

laterite blocks to determine the percentage of Silica,

Iron oxide and Aluminum oxide.

2.2. Compressive strength of laterite blocks and water

absorption:

Compressive strength of the laterite blocks has been

determined as per the guidelines of the IS: 3620-1979,

where as the water absorption have been determined as

per the procedure given in IS: 1121 (part 1- 1974). The

size of the blocks used for testing has been taken as

325mm x 220 mm x175mm, even though the code

recommends a size of 50mm x 50mm x50mm. This has

been done because of the difficulty in cutting of the

parent laterite blocks to achieve small blocks of size

50mmx50mx50mm, moreover the size of laterite blocks

used for testing matches with that of the size of laterite

blocks that are generally used for masonry construction

in the locality. The compressive strengths have been

obtained for all the four types of laterite blocks. As per

the procedure given in the code, the two faces are caped

using 1:1 cement mortar. The compressive strength has

been determined under dry and wet conditions. The wet

condition has been achieved by immersing the blocks in

water for 72 hours before testing. The blocks are

subjected to compressive loading in a compressive

testing machine. The compressive strength has been

determined both along the direction parallel to the

grains and perpendicular to the grains.

2.3. Stress-Strain behaviour of laterite blocks:

The Stress –Strain curve of laterite blocks (type III) are

determined under axial compression. Totally five blocks

have been tested in this program. The loads have been

applied in a compression testing machine and change in

length for every increment in the load has been

measured by using De-mech gauge. The loading has

been done till the specimens failed. With the help of

loads and change in length measured the values of

stresses and strains have been calculated. Plate 5 shows

the set up used for determining the stress-strain

behaviour of laterite blocks.

Plate5: Stress-Strain measurement setup

2.4. Moisture transport in laterite block and laterite

block masonry:

Laterite block is a porous material and has a tendency to

absorb water rapidly due to the capillary suction. The

rate at which a laterite block absorbs water, when

soaked in water also varies with time. In the initial

stages laterite blocks sucks water at high rate. The rate

of suction slows down after some time. This property of

laterite block has several ramifications on the behavior

of masonry. For instance, if the laterite block is dry at

the time of masonry construction, it is likely to absorb

significant amount of water from the mortar. This can

lead to a situation where the mortar becomes deficient

in water. The water deficiency can lead to two types of

problems.

a. Reduction of water cement ratio (w/c) of mortar

causing deficiency of water in the mortar so that

complete hydration will not occur. Incomplete

hydration will reduce the strength of masonry.

b. Reduction of bond strength between laterite block

and mortar. This will also affect the strength of

masonry.

It is thus clear that the moisture in laterite block and in

the mortar has to be carefully adjusted such that the

strength gain of mortar and laterite block-mortar

bonding do not suffer. This can be done by using

partially saturated blocks. Partial saturation of blocks

can be done by soaking of blocks in water before

construction.




2.4.1. Rate of moisture absorption of laterite

blocks:

The rate of moisture absorption of laterite blocks was

determined by soaking in water for different duration

of time (0,5min, 10min, 15min, 20min, 30min, 40min,

50min, 60min, 2hour, 3hour and 24hour). Five

specimens were used for each case.

2.4.2. Transport of moisture from mortar to laterite

block in masonry:

This study has been made by considering one type

(i.e, type III) of laterite blocks and three types of

cement mortars. Cement mortars considered for the

study are 1:3 cement mortar, 1:4 cement mortar and

1:6 cement mortar. The simple experiment suggested

by Groot (1993) to study the water transport

phenomena has been adopted. The steps of the

experiment are listed below.

a) The oven-dry blocks used in the experiment are

soaked in water for varying duration like 0

min, 5min, 10 min, 15min, 20min, 25min, and

30min.etc.

b) The partially saturated laterite block is covered

with fresh mortar to a thickness of 10mm on

top and another similarly saturated laterite

block is kept on the mortar.

c) The top laterite block is removed after one

hour and the mortar is scooped out and placed

in the container to note down the dry weight.

This indeed is been used to calculate the

moisture content of mortar.


3.1. Chemical Composition:

Table 2 gives the details of the chemical analysis results

for laterite blocks tested. From the test results it can be

observed that the silica content in the laterite blocks

varies from 58 to 76% and iron content varies from 14

to 26%. The alumina content is very less in all the

blocks and is in the range of 2 to 8 %. The maximum

iron content is for type III blocks at 26 %. For the same

blocks the silica content is 58 % and is the lowest

among all the blocks tested.

Table2: Chemical analysis

Name of

the quarry

Chemical Composition (%)

Silica Iron Alumina

Type I 76 14 2-4

Type II 71 16 3-5

Type III 58 26 6-8

Type IV 63 21 4-6

3.2. Compressive strength of laterite blocks and water

absorption:

Table 3 gives the details of the compressive strength

and water absorption results of the laterite blocks tested.

From the results it can be observed that the dry strength

varies from 0.9 MPa to 2.4 MPa and wet strength varies

from 0.5 MPa to 0.8 MPa for the blocks tested by

applying the load parallel to the grain. The same for

blocks tested by applying the load perpendicular to the

grains are 1.8 to 2.9 MPa in dry condition and 1.4 to 1.9

MPa in wet condition. In general the wet strength is

around 33% to 74% of dry strength. The blocks have

higher strength in a direction perpendicular to the grains

as compared to that of the strength parallel to the grains.

Important information revealed from this study is about

relationship between the iron content and compressive

strength. In all the cases the compressive strength

increases as the iron content increases and silica content

decreases. Same types of results have been obtained by

Manu et.al (2009) and Kasturaba et.al (2007) for the

laterite blocks they have tested.

Table3: Compressive strength and water absorption

Quarry Name

(Iron content

%)

Compressive strength in MPa Wet/Dry

strength

*

Wet/Dry

strength

**

Water

absorption

(%) Dry state

*

Dry state

**

Wet state

*

Wet state

**

Type I (14) 0.9 1.8 0.5 1.4 0.55 0.77 22.85

Type II (16) 1.4 2.1 0.55 1.55 0.39 0.74 20.40

Type III (26) 2.4 2.9 0.8 1.9 0.33 0.65 8.5

Type IV (21) 1.8 2.5 0.7 1.7 0.38 0.68 11.80

*Loading applied parallel to the grains

**Loading applied perpendicular to the grains

3.3. Modulus of elasticity of laterite:

The stresses and strains obtained from the tests

conducted on type III laterite blocks have been plotted

in Fig 1.0. From the graph it is seen that the stress strain

curve is linear upto 30 % of ultimate stress and further it

becomes nonlinear. The modulus of elasticity of laterite

blocks at 30% of ultimate stress is found to be 375MPa

and is comparable to those of Sujatha (2008) results




obtained from laterite blocks of Mangalore region. The

modulus of elasticity of bricks at 30% of ultimate stress

is in the range of 350MPa to 500MPa (Sarangapani)

(1998).

Fig1: Stress-Strain curve

3.4. Moisture transport studies in laterite block

masonry in different mortars:

3.4.1. Rate of moisture absorption in laterite blocks:

Fig 2 shows the variation in water content of laterite

blocks which are soaked in water for varying durations

of time. The curve represents the mean of 5 specimens.

The graph indicates that as the soaking period increases

the rate of water absorption also increases. In the initial

stages laterite blocks sucks water at high rate. The rate

of suction slows down after the moisture content in the

laterite block is 75% of its saturation value the laterite

blocks need to be immersed in water for 20 to 25

minutes to achieve this situation.

Similar types of results have been obtained by

Sarangapani (2008) for clay bricks and cement mortar.

Fig2: Water content of laterite blocks v/s duration of

soaking in water

3.4.2. Transport of moisture from mortar to laterite

block in masonry:

The variation of water-cement ratio of mortar with

moisture content of laterite blocks have been plotted in

the figures 3 to 5 for the cement mortars 1:3 cement

mortar, 1:4 cement mortar, 1:6 cement mortar. The

water-cement ratios of mortar after one hour of contact

with the prewetted blocks have been considered for the

study. From the figures it is clear that if the laterite

blocks are dry, most of the moisture in the mortar finds

its way into the block within one hour. In case of 1:3

cement mortar and 1:4 cement mortar the w/c ratio

reduces to a value less than 0.4. However for 1:6 cement

mortar water-cement ratio value is around 0.6. For

complete cement hydration the minimum water-cement

ratio required is 0.4. To achieve a water-cement ratio

value more than 0.4 in 1:3 cement mortar and 1:4

cement mortar the block should have a moisture content

of 75 % of its saturation value. As such the blocks have

to soak in water for 20-25 minutes to have a moisture

content of 75 % of its saturation value.

Fig3: Variation of water cement ratio of 1:3 cement

mortar with water content of laterite block








4. Conclusions:

1. The silica content varies from 58 to 76% and iron

content varies from 14 to 26% in the blocks tested.

The alumina content is very less in all the blocks

tested and are in the range of 2 to 8%.

2. The blocks have lesser strength along the grains as

compared to that of the strength perpendicular to

the grains.

3. In general wet compressive strength varies from

0.5MPa to 1.9MPa.The wet strength is around 33 to

74% of dry strength.

4. Compressive strength increases as the iron content

increases.

5. Laterite blocks have to be soaked in water for 20 to

25 minutes before the construction to achieve high

masonry strength.

5. Reference:

[1] IS 1121(Part 1)-1974 (Reaffirmed 2003). “Methods

of Test for Determination of Strength Properties of

Natural Building Stones – Compressive Strength”,

Bureau of Indian Standards, New Delhi, India.

[2] IS 1905-1987. “Indian Standard Code of Practice

for Structural Use of Unreinforced Masonry”,

Bureau of Indian Standards, New Delhi, India.

[3] IS 2250-1981 (Reaffirmed 2000). “Indian Standard

Code of Practice for preparation and use of

Masonry Mortars”, Bureau of Indian Standards,

New Delhi, India.

[4] IS:2720 (Part-XXV) ,Indian standard methods of

test for soils, Determination of silica sequioxide

ratio

[5] IS 3620-1979 (Reaffirmed 1998). “Indian Standard

Specification for Laterite Stone Block for

Masonry”, Bureau of Indian Standards, New Delhi,

India.

[6] ASTM C 97 (2002) Standard test methods for water

absorption and bulk specific gravity of dimension

stone, ASTM, Philadelphia, USA.

[7] ASTM C170 (2004) Standard test method for

compressive strength of dimensional stone. ASTM,

Philadelphia, USA.

[8] Gumaste K.S., Nanjunda Rao K.S., Venkatarama

Reddy B.V., Jagadish K.S. (2007). “Strength and

elasticity of brick masonry prisms and wallettes

under compression”, Materials and Structures 40:

pp. 241-253.

[9] Hemanth B Kaushik, Durgesh C Rai and Sudhir K

Jain (2007-a). “Uniaxial compressive stress strain

model for clay brick masonry”, Current Science,

Vol. 92, No. 4, pp. 497-501.

[10] Hemanth B Kaushik, Durgesh C Rai, and Sudhir K

Jain (2007-b). “Stress strain characteristics of clay

brick masonry under uniaxial compression”,

Journal of Materials in Civil Engineering, Vol.19,

No. 9, pp. 728-739.

[11] Study of weathering mechanisms of Malabar

laterite for building purposes”, Ph.D thesis,

Department of Civil Engineering, Indian Institute of

Technology, Madras, India.

[12] Sarangapani G. “Studies on the strength of Brick

masonry”, Ph.D thesis, Department of Civil

Engineering, Indian Institute of Science, Bangalore,

, India.

[13] Sureshchandra H. S., “Structural characteristics of

Masonry units, Mortar and Masonry”, Ph.D thesis,

Department of Civil Engineering, P.E.S. College of

Engineering, Mandya.

[14] Kasthurba A. K. and Santhanam M. (2005-b). “A

re-look into the code specifications for the strength

evaluation of laterite stone blocks for masonry

purposes”, Journal of Institution of Engineers

(India), Architecture Division, Vol. 86, pp. 1-6.

[15] Kasthurba A. K. and Santhanam M. (2006-a).

“Laterite as a prime masonry material for housing

construction in Malabar region of Western India”,

International Journal for Housing Science and its

Applications, Vol. 30, No. 3, pp. 183-194.

[16] Kasthurba A. K., Santhanam M. and Mathews M.S.

(2006-b). “Weathering forms and properties of

laterite building stones used in historic monuments

of western India, Structural Analysis of Historical

Constructions”, New Delhi, pp. 1323-1328.

[17] Kasthurba A. K., Santhanam M. and Mathews M.S.

(2007). Investigation of laterite stones for building

purpose from Malabar region, Kerala state, SW

India –Part 1: field studies and profile

characterization”, Construction and Building

Materials, 21, pp. 73-82.

[18] Sujatha unnikrishnan, M. C. Narashiman and Katta

venkataramana, Studies on uniaxial compressive

strength of laterite masonry prisms, International

journal of Earth science and Engineering, India,

April-2011, pp. 336-350




[19] Sarangapani G., Venkatarama Reddy B.V. &

Jagadish K.S. (2005). “Brick mortar bond and

masonry compressive strength”, Journal of

materials in civil engineering, Vol.17, No.2, pp.

229-237.

[20] Gidigasu M. D. Laterite soils Engineering, Elsivier

scientific publishing Co. 1976

[21] Arunkumar Bhat, Study of geotechnical and

strength parameters of laterite blocks in and around

karkalla taluk. B. E. Project report, NMAM

institute of technology, NItte, Karkala

[22] Shrinivasa Rao S., Venkatarama Reddy B. V. and

Jagadish K. S. (1995). “Strength characteristics of

soil-cement block masonry”, Indian Concrete

Journal, 69(2), pp. 127-131.

www.cafetinnova.org

Indexed in





ISSN 0974-5904, Volume 07, No. 01

February 2014, P.P.349-355


An Experimental Investigation on Some Strength Properties of

Light Weight Blended Aggregate Concrete

V BHASKAR DESAI1, A SATHYAM

2 AND K MALLIKARJUNAPPA

3

1Dept. of Civil Engineering, JNTUA College of Engineering, Anantapuramu – 515002, INDIA

2Archaeological Survey of India, Anantapur sub circle, JNTUA college of Engineering, Anantapuramu, INDIA 3Dharmavaram Municipality, Dharmavaram– 515671, JNTUA College of Engineering, Anantapuram, INDIA

Emails: [email protected], [email protected], [email protected]

Abstract: In this present study the behavior of light weight aggregate concrete has been studied by blending the

cinder and pumice aggregate. Blending of pozzolanic admixtures like fly-ash, silica fume, peanut ash, rice husk ash,

saw dust etc., is being used by the young and dynamic researchers in the recent years to enhance the properties like

compressive strength, shear strength, split tensile strength, flexural strength, impact strength, modulus of elasticity

and finally durability properties etc. But the limited study was initiated on blended aggregate concrete. Pumice is a

very light and porous igneous rock that is formed during volcanic eruptions was even used in roman structures.

Pumice is mined, washed and then used. Cinder is a waste material obtained from steel manufacturing units. It is

being used as a filler material for sunken slabs and also being used for structural purposes. Light weight aggregate

concrete is a concrete whose density varies from 300-1850 kg/m³ which is less than that of conventional concrete.

Light weight aggregate concrete is widely used in the construction industry. Its use is found in tall multi-storied

buildings, buildings with structural panels, roof beams for precast industrial sheds, long span bridges etc.

In this experimental investigation an attempt is made to study the strength properties of light weight blended

aggregate cement concrete by combining both the pumice and cinder in different proportions of 0, 25, 50, 75, 100 by

volume of concrete. By using these combinations the properties such as compressive strength, split tensile strength,

modulus of elasticity etc., are studied.

Key words: Cinder, pumice, light weight aggregate, compressive strength, tensile strength and youngs modulus.

1. Introduction:

The advancement in the new construction materials has

lead to develop high strength materials, which are

generally selected to reduce the weight of the

construction. Also the developments in the stress

analysis methods enable a more reliable determination

of local stresses in the materials, which permit safety

factors to be reduced resulting in further weight savings.

This induces low margins of safety for the structures

designed with high strength materials. But the service

stresses with aggressive environment may be high

enough to induce cracks, particularly if preexisting

flaws or high stress concentrations are present with in

the materials. As the residual strength of any structural

material under the presence of cracks is low, when small

cracks exists, the structures designed with high strength

materials may fail at stresses below the highest service

stresses for which they are designed.

2. Review of Literature:

Here the brief review of available studies related to the

present strength properties of cementitious materials is

presented. The review covers the study on strength

parameters investigated analytically and experimentally,

light weight aggregate concrete properties etc.,

Weigler, H. and Karl, S. Stahlleichtbeton (1) reported

that air entraining agents can be used with light

weight aggregate Concrete. It’s use reduces the

density proportionally to the weight of the paste it

replaces, enhances the workability and reduces the

segregation and bleedings.

H.Bomhard (2) had reported that Structural light

weight aggregate concretes are considered as

alternatives to concretes made with dense natural

aggregates because of the relatively high strength to

unit weight ratio that can be achieved.

In Japan JASS (3) reported that, light weight

concretes do not specify any density values, and

properties are only provided for concrete made with

light weight coarse and fine aggregates.

Clarke, J.L (4) Tensile strength of concrete is important

when considering cracking. Light weight aggregate

concrete presents a flexural and tensile splitting

350 An Experimental Investigation on Some Strength Properties of Light Weight Blended

Aggregate Concrete



strength slightly inferior to that of normal weight

concrete of the same compressive strength.

Owens, P.L. (5) stated that light weight aggregate

concrete has been used for structural purposes since

the 20th

century. The Light weight aggregate

concrete is a material with low unit weight and

often made with spherical aggregates. The density of

structural Light weight aggregate concrete typically

ranges from 1400 to 2000 kg/m³

compared with

that of about 2400 kg/m³

for normal weight

aggregate concrete.

Yang and Huang (6), stated that naturally available

light weight aggregate such as volcanic cinder,

pumice and scoria possess high strength due to the

higher porosity.

Khandaker M. Anwar Hossain, (7) concluded that the

volcanic pumice concrete (VPC) has sufficient strength

and adequate density to be accepted as structural

lightweight concrete and compared to control

concrete, the volcanic pumice concrete has lower

modulus of elasticity and has more permeability and

initial surface absorption.

3. Materials with properties used in the

Investigation:

Table1: Properties of Materials

Sl.

No

Name

of the

materia

l

Properties of material

1

OPC –

53

Grade


Initial setting time 60 min

Final Setting time 489 min

Fineness 4 %

Normal consistency 33.50 %

2

Fine

Aggrega

te

passing

4.75mm

sieve


Fineness modulus 3.24

3

Pumice

Agg.

passing

20 – 10

mm



Bulk density

compacted

570

Kg/m3

4

Cinder

Agg.

passing

20 – 10

mm



Bulk density

compacted

1050

Kg/m3

Constituent materials used are shown in plate. 1

3.1. Properties of Cinder:

The surface of the cinder is usually rough and highly

porous due to mineral structure. No physical testing is

usually performed to quantify the angularity of the

material, however it is visually classified as having

100% crushed face. The water absorption for cinder is

around 1.5%.

3.2. Properties of Pumice:

Pumice is a natural sponge-like material of volcanic

origin composed of molten lava rapidly cooling and

trapping millions of tiny air bubbles. Pumice is the only

rock that floats on water, although it eventually

becomes waterlogged and sinks. Since pumice is a

volcanic rock, and retains its useful properties only

when it is young and unaltered, pumice deposits are

found in areas with young volcanic fields.

3.3. Water:

Potable water was used in this experimental work.

3.4. Need of Bleded Aggregate:

Many research scholars have studied the behavior of

concrete and strength properties by using single light

weight coarse aggregate without blending. Some of

them have studied the strength properties by replacing

light weight coarse aggregate with conventional

aggregate. But very little study is reported on strength

properties by using multiple light weight coarse

aggregates. That is why in the present investigation an

experimental study has been conducted by blending two

types of light weight coarse aggregate i.e. cinder and

pumice with different percentages.

The blending of light weight coarse aggregate is also

useful in many places where the conventional aggregate

are not available and also where the special concrete is

needed i.e. for insulation purpose etc.

4. Expermental Investigation:

An experimental study has been conducted on

concrete with partial replacement of light weight

coarse aggregate i.e., Cinder by another light weight

aggregate i.e., Pumice with few different volumetric

fractional additions ranging from 0% to 100%.

Concrete of M20 design mix is used in the present

investigation. The analysis of results has been done to

investigate the strength properties.

4.1. Casting of Specimens:

The M20 concrete mix was designed using ISI method

which gives a mix proportion of 1:1.55:3.04 with water

cement ratio of 0.50. Five different mixes studied are

designated as follows:

351 V BHASKAR DESAI, A SATHYAM AND K MALLIKARJUNAPPA



Table2: Details of Mix Designation

Name of

the Mix

Replacement of Coarse

Aggregate by Volume

percentage

No of

specime

ns cast Cinder

Aggregate

Pumice

Aggregate

B-4 100 0 9

B-3 75 25 9

B-2 50 50 9

B-1 25 75 9

B-0 0 100 9

Total 45

To proceed with the experimental program initially

steel moulds of size 150x150x150 mm were cleaned

and were brushed with machine oil on all inner faces

to facilitate easy removal of specimens afterwards. To

start with, all the materials were weighed in the ratio

1:1.55:3.04. First fine aggregate and cement were

added and mixed thoroughly and then coarse aggregates

with Cinder and partially replaced Pumice was

mixed with them. All of these were mixed thoroughly

by hand mixing. Each time 3 cubes and 6 cylinders were

cast. For all test specimens, moulds were kept on the

plat form vibrator and the concrete was poured into

the moulds in three layers each layer being

compacted thoroughly with tamping rod to avoid

honey combing. Finally all specimens were vibrated

on the table vibrator after filling up the moulds up

to the brim. The vibration was effected for 7 seconds

and it was maintained constant for all specimens and

all other castings. The specimens were demoulded

after 24 hours of casting and were kept immersed

in a clean water tank for curing. After 28 days of

curing the specimens were taken out of water and

were allowed to dry under shade for few hours.

5. Testing of Specimens:

The cube and cylindrical specimens were kept

vertically between the compressive platens of the

testing machine. The load was applied uniformly

until the specimens fails, and ultimate loads were

recorded. The test results of cube and cylinder

compressive strengths are furnished in table 3. This

setup is presented in plate 2 and 3. An attempt to find

out the modulus of elasticity has been done in a

3000kN automatic compression testing machine with

0.5kN/sec rate of loading. The results of modulus of

elasticity are furnished in table 5. The cylindrical

specimen was kept horizontally for finding the split

tensile strength. The test setup is shown in plate 5.

5.1. Discussion of Crack Pattern:

In case of cubes under compression test initial cracks

are developed at top and propagated to bottom with

increase in load and the cracks are widened at failure

along the edge of the cube more predominantly along

the top side of casting. In case of cylinders under

compression cracks are developed at top and bottom and

with the increase in load the cracks got widened at

central height. In case of cylinders subjected to split

tensile strength the cylinder is splitted into two pieces.

5.2. Discussion of Test Results:

In the present study the discussion of test results is as

follows.

5.2.1. Influence of Blended Aggregate Concrete on

Cube Compressive Strength:

The details of compressive strength are presented in

table 3. With increase in the percentage of replacement

of Cinder by Pumice aggregate, the compressive

strength of cube is found decrease continuously up to

100% replacement of Cinder by Pumice. The

variation of cube compressive strength of concrete with

the percentage of pumice replacing the cinder aggregate

is presented in fig 1. From them, it is observed that with

100% replacement of cinder by pumice the compressive

strength gets decreased by 47.53%.


Cylinder Compressive Strength:

The cylinder strength results are presented in table 3.

With increase in the percentage of replacement of

Cinder by Pumice aggregate the compressive strength of

cylinder is found to decrease continuously up to 100%

replacement of Cinder by Pumice and this variation is

presented in fig 2. From them, it is observed that with

100% replacement of cinder by pumice the cylinder

strength gets decreased by 40.40%.


Split Tensile Strength on Cylinder Specimens:

The split tensile strength results are presented in table 4.


Cinder by Pumice aggregate the split tensile strength

is found decreases continuously up to 100%

replacement of Cinder by Pumice. The variation

between split tensile strength and percentage of pumice

replacing cinder aggregate concrete is shown in fig 3.

From them, it is observed that with 100% replacement

of cinder by pumice the split tensile strength gets

decreased by 37.82%.


Youngs Modulus:


Aggregate Concrete



Table 5, presents the details of young’s modulus

calculated. The theoretical values of young’s modulus

are calculated by two approaches.

The first approach for calculating young’s modules used

the 28 days compressive strengths of blended aggregate

concrete mix by using I.S.Code formula9, because in the

absence of specific formula for light weight concrete.

Ec = 5000√fck N/mm2

where

fck = 28 days characteristic compressive strength in

N/mm2

Secondly by using another formula suggested by

Takafumi Naguchi et.al9 which is given below.

Ec = k1 x k2 (1.486 x 10-3

) x σb⅓ x γ

2 N/mm².

Where

k1 = correction factor for coarse aggregate i.e. 0.95

k2 = correction factor for mineral admixture i.e. 1.026

σb = compressive strength of concrete in MPa.

γ = Density of concrete in kg/m3


Cinder by Pumice aggregate the young’s modulus is

found decrease continuously up to 100% replacement

of Cinder by Pumice. The theoretical values of

young’s moduli of blended aggregate concrete are

continuously decreased by increasing pumice aggregate.

The corresponding graphical variation is presented in

fig 4. By observing the results it may be seen that the

values calculated from I.S.Code formula are higher than

those calculated by other empirical formula, and have

good agreement between two approaches.


Density:

The results of density are presented in table 6. The

variation between density and percentage of Pumice

replacing Cinder aggregate concrete is shown in fig.5.

From the above it is observed that, with the addition

of Pumice the density of specimens decreases

continuously up to 100% replacement of Cinder by

Pumice. The density of pumice aggregate concrete over

cinder aggregate concrete gets decreased by 30.50 % at

100% replacement.

Table3: compressive strength results

Sl.

No

Name

of the

mix

Replacement of Coarse Aggregate

by Volume percentage

Cube

compressive

strength

(N/mm²)

Cylinder

compressiv

e strength

(N/mm²)

Percentage of

increase or decrease

in compressive

strength

Ratio of

cylinder to

cube

compressive

strength Cinder

Aggregate (CA)

Pumice

Aggregate (PA) Cube Cylinder

1. B-4 100 0 24.53 15.00 0.00 0.00 0.61

2. B-3 75 25 20.71 14.73 -15.57 -1.80 0.71

3. B-2 50 50 16.19 12.05 -33.99 -19.67 0.74

4. B-1 25 75 13.39 10.84 -45.41 -27.73 0.81

5. B-0 0 100 12.87 8.94 -47.53 -40.40 0.69

Table4: split tensile strength results

S.

No

Name of

the mix

Replacement of Coarse Aggregate by

Volume percentage Split tensile

strength

(N/mm²)

Percentage of increase or

decrease in split strength Cinder

Aggregate(CA)

Pumice

Aggregate(PA)

1. B-4 100 0 2.38 0.00

2. B-3 75 25 1.97 -17.23

3. B-2 50 50 1.72 -27.73

4. B-1 25 75 1.52 -36.13

5. B-0 0 100 1.48 -37.82




Table5: young’s modulus

S.

No

Name of

the mix


Volume percentage Young’s

modulus

E=5000√fck6.2.3.1

(N/mm²)

Young’s modulus

Ec=k1 x k2 (1.486 x

10-3

) x σb⅓ x γ

2

(N/mm²)

K1=0.95,k2=1.026

Cinder

Aggregate(CA) Pumice Aggregate(PA)

1. B-4 100 0 2.48*104 2.15*10

4

2. B-3 75 25 2.28*104 1.71*10

4

3. B-2 50 50 2.01*104 1.44*10

4

4. B-1 25 75 1.83*104 1.05*10

4

5. B-0 0 100 1.79*104 0.84*10

4

Table6: density results

S.

No

Name of

the mix


Volume percentage Density

(kg/m³)

Percentage of increase or

decrease in Density Cinder Aggregate

(CA)

Pumice Aggregate

(PA)

1. B-4 100 0 2262 0.00

2. B-3 75 25 2072 -8.39

3. B-2 50 50 1979 -12.51

4. B-1 25 75 1749 -22.68

5. B-0 0 100 1572 -30.50

Cement sand

Pumice cinder

Plate1: ingredients of concrete

Plate2: plain cubes

plate3: plain cylinders


Aggregate Concrete



Plate4: test set up for testing cubes

plate5: test set up for testing cylinders

FIGS:

0 25 50 75 100

0

5

10

15

20

25

30

35

40

45

Cu

be

co

mp

ress

ive

str

en

gth

(N

/mm

²)

% of Pumice replacing Cinder aggregate

Scale

X-axis 1unit = 25%

Y-axis 1unit = 5N/mm²

Fig1: Variation between Cube compressive strength

and percentage of Pumice replacing Cinder aggregate

0 25 50 75 100

0

5

10

15

Cyl

ind

er

co

mp

ressiv

e s

tre

ng

th in

N/m

m2

Percentage 0f pumice aggreagte replacing cinder aggreagte

Scale

x-axis 1 Unit = 25%

y-axis 1 Unit = 5 N/mm2

Fig2: Variation between Cylinder compressive strength

and percentage of Pumice replacing Cinder aggregate

0 25 50 75 100

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Sp

lit te

nsi

le s

tre

ng

th (

N/m

m²)

% of Pumice replacing cinder aggregate

Scale

X-axis 1unit = 25%

Y-axis 1unit = 0.5N/mm²

Fig3: Variation between Split tensile strength and

percentage of Pumice replacing Cinder aggregate

0 25 50 75 100

0

5000

10000

15000

20000

25000

Yo

un

gs M

od

ulu

s in

N/S

q.m

m

Percentage of Pumice Replacing cider aggregate

Emperical formula

I.S.Code

Fig4: Variation between Young’s modulus and

percentage of Pumice replacing Cinder aggregate




0 25 50 75 100

0

250

500

750

1000

1250

1500

1750

2000

2250

2500

2750

3000D

en

sity (

kg

/m³)

% of Pumice replacing cinder aggregate

Scale

X-axis 1unit = 25%

Y-axis 1unit = 250kg/m³

Fig5: Variation between density and percentage of

Pumice replacing Cinder aggregate

0 25 50 75 100

0

2

4

6

8

10

12

14

16

18

20

22

24

26

28

30

Co

mp

ress

ive

str

en

gth

in N

/mm

2

Percentage of Pumice replacing cinder aggreagte

Cylinder

Cube

scale

x-axis 1 Unit = 25%

y-axis 1 Unit = 2 N/mm2

Fig6: The variation between compressive strength and

percentage of pumice replacing cinder aggregate

6. Conclusions:

From the limited experimental study the following

conclusions are seem to be valid:

From the study it is concluded that the densities

have decreased continuously with the increase

in percentage of Pumice.

The cube compressive strength has decreased

continuously with the increase in percentage of

Pumice.

The cylinder compressive strength has decreased


Pumice.

The split tensile strength has decreased


Pumice.

The young’s modulus has decreased continuously

with the increase in percentage of Pumice.

It is concluded that the results of young’s moduli

arrived from I.S.code formula are observed to be

more than the results arrived from the empirical

formula

The ratio of cylinder compressive strength to cube

compressive strength is observed to coincide more

or less with that conventional aggregate concrete

i.e. 0.85.

7. Reference:

[1] Weigler, H and Karl, S. Stahlleichtbeton.

Bauverlag GMBH, Wiesbaden and Berlin, pp. 38-

43, 1972.

[2] H. Bomhard, Light weight concrete structures,

potentialities, limits and realities, The Concrete

Society, The Construction Press, Lancaster, UK,

1980, pp. 227–290.

[3] JASS 5 (Revised 1979): Japanese Architectural

Standard for Reinforced Concrete, Architectural

Institute of Japan, Tokyo, 1982 (March).

[4] Clarke, J.L. Design Requirements. Structural

Light weight Aggregate Concrete, Chapman &

Hall, London, pp. 45-74, 1993.

[5] Owens, P.L. (1993). “Light weight aggregates

for structural concrete,” Structural Light weight

Aggregate Concrete, Chapman & Hall, London,

pp.1-18.

[6] Chi, J.M., Huang, R., Yang, C.C., and Chang. J.J.

"Effect of aggregate properties on the strength and

stiffness of lightweight concrete”. Cement &

Concrete Composites 2003. L. Cavaleri, N.

Miraglia and M. Papia, “Pumice Concrete for

structural wall panels”, Engineering structures, Vol.

25, No. 1, Jan 2003, pp. 115-125.

[7] Khandaker M. Anwar Hossain, “Properties of

volcanic pumice based cement and lightweight

concrete”, Cement and concrete research, vol.

34, No. 2, febrauary 2004, pp. 283-291.

[8] I.S.Code 456-2000 “Code of practice for plain and

reinforced concrete” Bureau of Indian Standards,

New Delhi.

[9] Takafumi Noguchi, Fuminori Tomosawa, Kamran

M. Nemati, Bernardino M. Chiaia, and Alessandro

P. Fantilli (2009) A Practical Equation for Elastic

Modulus of Concrete. ACI structural journal/Sept-

Oct 2009, technical paper title no. 106-SXX.

www.cafetinnova.org

Indexed in





ISSN 0974-5904, Volume 07, No. 01

February 2014, P.P.356-362


Estimation of Methane from Flooded Paddy fields in Andhra

Pradesh

ANUP MATTHEW, ATUL V RAO AND VENKATA RAVIBABU MANDLA School of Mechanical and Building Sciences, VIT University, Vellore-632 014, TN, INDIA

Email: [email protected], [email protected], [email protected]

Abstract: Estimation of methane emission from paddy fields are necessary to understand the emissions at the

national or global scale, develop representative models and devise appropriate mitigation strategies to counter

climate change and its consequences. The objective of the present study is estimation of methane in the coastal

region of Andhra Pradesh, India using LANDSAT ETM+ image classification based on standard algorithm, the

temperature factor was obtained and methane emission estimated in the study area was 0.008Tg/ y. The estimates

are close to the estimates mentioned in available literature. Field based validation studies are necessary for

determining the accuracy since most literature differ in their methane estimates and modeling requires accurate

methane estimates. The present study is a preliminary step for developing the regional climate models which can be

used for climate, environmental and agricultural management studies.

Key words: climate change, greenhouse gas, methane flux, methane emission factors, land surface temperature,

maximum likelihood classification.

Introduction:

Methane emissions and its sources are being curiously

studied since one unit mass of methane has a radiative

effect 21 times greater than one unit mass of CO2 which

assigns it a very high greenhouse warming potential

(GWP). According to Intergovernmental Panel on

Climate Change [1] between 1750 and 1995,

atmospheric concentration of methane rose 150 % (as of

1998) which translates to increase from 700 to 1,745

parts per billion (ppb) by volume. Anthropogenic

methane source include activities in agriculture,

transportation, energy, industry and waste disposal. On

a global scale, methane emissions from agricultural

sources are projected to be 3135.75 Mt of CO2eq p.a.

making it the highest (50.63%) of anthropogenic source.

Agricultural methane emissions range from enteric

fermentation (59.84% of emissions from agricultural

sector), to the emissions from rice cultivation,

agricultural activities and manure management [2]. Net

GHG emissions were 1727.7 million tons (Mt) of CO2

eq. from India in 2007. The sources were energy sector

(57.8%), industrial (21.7%), agricultural (17.6%) and

waste (3.0%) sectors. Agricultural sector contributes

total emission of 334.4 Mt CO2 eq., the major sources

are enteric fermentation

(63.4%), rice cultivation (20.9%), agricultural soils

(13.0%), manure management (2.4%) and on-field

burning of crop residues (2.0%) [3]. Flooded paddy

fields contribute nearly 10% of total global methane

emissions and nearly 26% of the global anthropogenic

methane budget making paddy cultivation a significant

contributor to global warming. National level

measurements of methane from rice fields range from

0.1% (USA, 2005) to about 9.8% in India (2006) [4].

The quantity of methane released from rice paddies is

influenced by various factors like quantity of organic

materials, temperature, pH, soil quality and water

management practices [4].

In India, total area under rice cultivation is 42.32 Mha.

Large areas, anaerobic conditions, reducing

environments and organic loads in paddy cultivated

areas are major terrestrial sources of methane. Organic

materials degrade in anoxic conditions in flooded paddy

fields and release methane. Simulated annual emissions

from 42.32 Mha of rice fields of India were reported to

be 2.07 Tg y-1 [5].

Three major types of rice cultivation are dry land rice,

irrigated rice and rain fed rice. Irrigated rice constitutes

half of the rice growing area and produces more

methane due to constant water stagnation which favors

methane production. Rain fed rice varieties’ methane

generation varies with space and time and probably

emits lesser methane because of dry-moist cycles. The

irrigated fields are divided into continuously flooded

and intermittently flooded and constitute 16% (6.77

Mha) and 37% of the total cultivation area respectively

[6]. Rice is the most important staple food crop as it is

the staple food for 3.23 billion people worldwide. The


357 ANUP MATTHEW, ATUL V RAO AND VENKATA RAVIBABU MANDLA



global annual rice production is estimated to increase

from 518 million tonnes in 1990 to 760 million tonnes

in 2020 to feed the growing population and this implies

there will be expansion and intensification of rice

cultivation in the coming years [7]. This increased

production is likely to increase methane fluxes in the

atmosphere if current technologies are continued.

Estimation of methane emission from flooded paddy

fields will aid in the assessment of emissions at the

national or global scale, develop representative models

and devise appropriate mitigation strategies to counter

climate change and its consequences.

Andhra Pradesh is a major rice producing state with

total of 4.75Mha of rice producing area amounting to

nearly 10% of India’s total paddy area under cultivation.

The major rice growing districts are in the coastal

region of Andhra Pradesh between the Eastern Ghats

and the Bay of Bengal, and from the northern border

with Odisha to south of the delta of the Krishna River

[8]. The 9 districts in this region and their area,

production and yield with respect to paddy during the

periods 1999-2000 are given in Table 1. Among these

districts, agricultural data indicates that West Godavari

and Krishna districts have vast areas of paddy fields.

Rice is cultivated under irrigated eco-system under

canals (52%), tube wells (19.31) tanks (16.2%), other

wells (8.8%) and other sources (3.7%) and being low

lying districts they are prone to flooding and rain water

stagnation. Rice is grown in 23 districts of Andhra

Pradesh and 14 of these are intensive rice cultivation

regions.[8, 9] Studies by state agencies indicate an

increase in average earth temperature and corresponding

increase in sea-surface temperature which is causing

further volumetric expansion of the sea [8,9]. Agarwal

and Garg [10] have performed methane estimation for

wetlands in Gujarat. Moderate resolution imaging

spectroradiometer (MODIS) sensor with thermal

channels/bands (31&32) and optical channels/bands (1,

2, &3) was used and a methodology developed to

estimate methane emissions from various land types.

The model was based on 2 factors- temperature and

productivity. Temperature was obtained using constant

emissivity method (CEM) and productivity from

Sheppard et al [12]. The observed land surface

temperature (LST) values were validated using MODIS

satellite data imageries. The paper acknowledges the

lack of objective global methane estimates, which may

be due to lack of spatial-temporal data of wetlands.

Methane emissions from natural wetlands have been

estimated in New South Wales in Australia using

Landsat enhanced thematic mapper plus (ETM+)

satellite data and attempted to estimate the relationship

between methane emission and temperature increase.

Process based methane emission model dependent on

productivity, wetland area, methane flux and

evaporation-precipitation ratio was developed and

annual methane emissions were estimated. Validation

was done by performing ground studies. The

temperature, pH, moisture content and rainfall

variations have not been considered and the spatial

resolution for classification is coarse [13].

Bhatia et al [14] have estimated state wise methane

emissions by incorporating certain correction factors

(water management, cultivar, soil type, and fertilizer)

for methane emission methodology proposed by IPCC

in 1994-1995. The methane emission was estimated to

be 2.9 Tg y-1. The total area under rice cultivation was

42.24Mha of which 10.97 Mha was irrigated,

continuously flooded and contributed 1.4 Tg y-1

methane which is 47% of national emissions. State wise

methane generation depends on flooding period which

affects redox potential of the soil to create anaerobic

mechanisms. The temporal studies of redox potential

vary based on soil types. Lowland paddy cultivated

areas were found to emit 1.7Tg y-1 (continuously

irrigated) and 0.6 Tg y-1 over 6.77 Mha and 9.73Mha

respectively [6]. The study acknowledges that the data

available for methane emissions show variations and

there are certain inconsistencies. Cao et al [15] state that

methane emissions vary largely on spatial temporal

scales making accurate methane emission estimation

based on Net productivity value and point measurement

correlations challenging. Sheppard et al [12] have

proposed methane emission factors for various land

classes assuming methane emissions from tropical rain

forests to be nearly constant and all ecosystems are

normalized with respect to it. Methane flux from

respective ecosystems is used to estimate terrestrial

methane emissions. The methane productivity for rice

paddy ecosystem is 55g/m2 year which corresponds to

an emission rate of 39x1012 g/year.

Table1: District wise paddy cultivation area,

production and yield data for coastal region of Andhra

Pradesh as in 1999-2000 [11]

District Area

(ha)

Yield

(kg/ha)

Production

(Tonnes)

Srikakulam 190400 1724 328300

Vizianagaram 134000 1905 255300

Visakhapatnam 106100 1336 141800

East Godavari 414300 3197 1324400

West Godavari 462000 3177 1468000

Krishna 403000 3215 1296600

Guntur 317700 3235 1027900

Prakasam 140400 2482 348500

Nellore 197300 3304 651800

358 Estimation of Methane from Flooded Paddy fields in Andhra Pradesh



The Study Area, Data used and Methodology:

Andhra Pradesh adjoins the Bay of Bengal and has a

coastline of 974 km2 and continental shelf area of

33,227 km2 which narrows down from north to south.

The study area is the north eastern district of Andhra

Pradesh, India. The geographical extent is between

latitudes of 150 N and 18

030’ N and longitudes of 80

0 E

and 820 30’E.

Fig1: Study area highlighting with political map of Andhra Pradesh along coastal districts

Agricultural regions are vast, with paddy cultivation

being the chief activity. The total unclassified region is

62567.8 km2, including the sea, inland water bodies,

forests and pockets of urban settlements.

Approximately, 12470.73 km2 of agricultural lands

(nearly 19.93 % of the total region) has been considered

for methane estimation since it appeared to be an area of

flooded paddy cultivation type. As per the literature

accessed, areas under the irrigated-flooded paddy

cultivation display high methane emissions. The

estimated methane flux from cultivated paddy lands is

approximately 55 g/m2/year [12]. The classification

carried out for the study area has been shown below in

figure 2

The Landsat ETM+ data was used for this analysis. The

spectral bands used to estimate the methane emission

from the study area were the optical, visible, near-

infrared and thermal bands. The bands that were used to

extract the class information are the visible and the

near-infrared while the thermal band was used to

estimate the land surface temperature. Landsat ETM+

data of 28 October 2000 were used to estimate methane

emission from the wetlands. The image acquired were

cloud free and hence could be used for the analysis

purpose without encountering masking of regions by

clouds.

Coastal Region




Fig2: land use / land cover of the study area


The methane emission from tropical wetlands is

estimated from a two parameter model as given in eq

(1). The two parameters are temperature and

productivity ratio. The temperature data is compiled into

a factor known as T factor given by eq (6). Productivity

ratio was obtained from Sheppard et al [12]

Fig3: map of different agricultural areas with standing water (cyan in color).




The detailed method of obtaining each parameter for the

methane model is given below:

Estimation of Methane:

The flooded agricultural region, typical of paddy

cultivation, was chosen for evaluating the emission of

methane. The methane emission from the agricultural

area was estimate by using eq (1)

PAFEEtobsCH ,

4 ......................eq (1)

Where,

4CHE = The methane estimated in (g/year)

obsE = The observed methane flux from the class

(km2/year)

A = The Area of the class

P = Productivity Ratio.

The estimate methane is generally expressed in units of

Tg (tera grams, 1 tera gram = 1012

grams). The area of

each class was estimated. The paddy cultivation area

with standing water was estimated to be around

1226km2. The areas of each of the classes have been

given below:

The inland water covers about 3447 km2, the forested

area is 10845.2 km2, Agricultural land is 12470.73 km

2

of which the Agricultural land flooded with water is

1266 km2 and the remaining 11204 km

2 of Agricultural

land was divided into Agri 1 (3908.4 km2) and Agri 2

(7296 km2). The soil cover was found to be covering an

area of 19091.4 km2 and the area under urban land was

1245.5 km2. The area covered by sea was calculated as

15170.8 km2.

The study area was a very important parameter in the

estimation of methane. The classification was carried

out using maximum likelihood supervised classification.

This included training the classifier with region of

interest (ROI). Supervised classification was obtained

since it was possible to verify the land use classification

on field from interaction with personnel’s from the area

and hence it was possible to train the classifier for the

classification. It was decided to use the maximum

likelihood classification as it often has lower variance

than other methods and they use most of the sequence

information.

The overall accuracy of the classification on the basis of

ground truth ROI’s was obtained as 97.3389%.

Land Surface Temperature (LST) Estimation:

The thermal bands 62 of LANDSAT ETM+ data was

used to calculate the land surface temperature of the

given area. The digital number (DN) in satellite data of

the thermal band 62 was converted to Radiance which

was then converted to temperature to give the land

surface temperature by the following eq (2)

LMINQCALLMINDNXQCALLMINQCALLMAX

LMINLMAXL

)(

)

)( ---(2)

Where, for thermal band 62

65.12LMAX

200.3LMIN

0.255QCALMAX

0.1QCALMIN

The following expression was entered for converting the

DN to radiance for the area of interest

((12.650-3.200)/ (255.0-1.0))*(B6-1.0) + 3.200

[Where B6 is the input of thermal band 62].

The radiance value was then converted to temperature

by the eq (3) Planck’s Radiance Function [20]

)

1(

)(25

1

Te

C

CTB

…… ..eq (3)

Where C1 = 1.19104356 X 10-16

W m2 and C2 =

1.43876869 x 10-2

mK

The ground temperature can be theoretically obtained

by eq (4)

)1)(

1ln(

5

2

2

TB

C

C

T

… …...eq (4)

The equation is shrunk by making the substitution

51

1 C

K and

22

CK and satellite measured

radiant intensity LTB )( we get the temperature as

eq (5)

)1ln( 1

2

L

K

KT ……eq (5)

Where, K1 = 666.09 W m-2

sr-1

μm-1

and K2 = 1282.71

kelvin.

The equation (1282.71D/(alog(666.09D/B6+1D)))-273

is used to calculate temperature where B6 is the

radiance value calculated above and D forces the

numbers into double precision.

The value of temperature returned is in Celsius. The

temperature values are then used to calculate the T

factor which is given by eq (6) & (7)

)(

)(

s

s

TF

TFTfactor …….eq(6)




)23(334.0

)23(334.0

2

2

1)(

T

T

se

eTF ……eq(7)

and )( sTF is the mean value of F(Ts). The T factor (Ft)

was evaluated for the region of interest (flooded

agriculture) and was found to be 1.000007.

Productivity Ratio:

The productivity ratio P is given as

)inf(

)cos(

oresttropicalraNPP

ystemeNPPP ……eq (8)

NPP is the net primary productivity and P is the ratio of

NPP of a given ecosystem to the NPP of tropical

rainforest. The ratio is to the NPP of a tropical rainforest

because it is assumed that the NPP of tropical rainforest

would be constant throughout the year. The productivity

ratio for the flooded agricultural land was calculated

from eq (9).

05.135.23

4.155

)inf(

)(

oresttropicalraNPP

parryfieldcultivatedNPPP

.....eq(9)

The productivity ratio of the area is 0.24.

Observed Methane Flux:

Observed methane flux is obtained from [12] for

cultivated paddy fields as 27.85 g/m2/year.

Using the values of observed methane flux, area of the

field, productivity ratio and T factor, the methane

emission is evaluated as

PFAEE tobsCH 4

24.0000007.11033.122685.27 6

year

gECH 8196646576

4

year

TgECH 0081966.0

4

The methane emissions from the flooded agricultural

fields of area 1226.33 km2, considered as paddy fields

have been estimated to be 0.0082 Tg/year by the present

study. IPCC based model study in Andhra Pradesh

indicates that from 34560 km2

of irrigated paddy fields,

the annual methane emissions are 0.35 Tg/year [16]

which roughly converts to 0.0123 Tg/year for the area

considered in our study which is higher than that

determined by the present study. Parashar et al [6] have

assessed that of the 97300 km2

of irrigated paddy fields

in India, the methane emissions are 0.6 Tg/year. A

simple interpolation to 1226.33 km2

yields a result of

0.007562 Tg/year which is very close to the value

obtained from the present study. Conclusive validation

geospatial studies like the present study is possible by

conducting thorough field studies at regular, pre-

determined intervals spanning all cropping seasons. The

field studies must also include the parameters such as

rice cultivar, water management, soil types, soil

characteristics, soil quality management and water

management practices since these are known to affect

the methane emissions. The inclusion of such

parameters will help approximately quantify the effects

of these- both in conjunction with, and independent of

the other parameters in particular and also analyze the

methane emissions in relation to the overall agricultural

management practices and climate conditions. When

field studies are combined with such geospatial based

studies, a comprehensive regional climate model

applicable to that specific region can be developed. The

regional climate model can provide a mechanistic basis

for spatio-temporal variations in methane emissions

which can serve as the basis for the development of

interactive software based decision support system for

undertaking sustainable agricultural practices.

Conclusions:

Methane emissions of 0.008 Tg / year were found for

the chosen study area which was under flooded paddy

cultivation. Validation of these estimates by appropriate

ground studies is necessary to develop pertinent model

of methane estimation that would eventually eliminate

the need for regular field studies which are tedious,

expensive and time consuming. The present study is

preliminary step for development of a regional climate

model to study relations between climate, agriculture

and GHG emissions. Methane mitigation in paddy

cultivation is possible by certain sustainable agricultural

practices which reduces emissions from rice cultivation,

reduces GHG additions and prevents economic losses to

the farmers by elimination of inefficient and

unsustainable farming practices. The regional climate

model can be used to analyze climatic factors that

influence agriculture; devise sustainable agricultural and

water management practices and a mechanism can be

developed for real time information dissemination to

guide the farmers on a regular basis towards sustainable

agriculture.

Acknowledgment:

Authors would like to thanks to anonymous reviewers

for their valuable suggestions.

Reference:

[1] IPCC (2007), Report on "Climate Change 2007-

Mitigation of Climate Change" Working Group III

Contribution to the Fourth Assessment.

[2] Karakurt I, Aydin G and Aydiner K (2012),

Sources and mitigation of methane emissions by




sectors: A critical review. Renewable Energy 39

40-48.

[3] Pathak H and Aggarwal P K (Eds.) (2012), Low

Carbon Technologies for Agriculture: A Study on

Rice and Wheat Systems in the Indo-Gangetic

Plains. Indian Agricultural Research Institute.

ISBN: 978-81-88708-83-3.

[4] Singh S, Singh J S and Kashyap A K, (1999)

Methane flux from irrigated rice fields in relation to

crop growth and N-fertilization.Soil Biology and

Biochemistry 31 1219-1228.

[5] ICAR (2009), Annual report on “Chapter 4-

Climate” Department of Agricultural Research and

Education- Indian Council of Agricultural

Research, New Delhi.

[6] Parashar D C, Garg S C and Mitra, A P (1999),

Parameters controlling emission and production of

methane from Paddy Fields. National Physical

Laboratory, Pune.

[7] FAO (2004) Reports on “The State of Food

Insecurity in the World”.

[8] Report on “Rice in Andhra Pradesh 2010”

published by Andhra Pradesh State Agriculture

Department, Hyderabad.

[9] SAPCC (2011), Report on “State Action Plan on

Climate Change for Andhra Pradesh, 2011.”

Prepared by Environment Protection Training and

Research Institute. 16th July, 2011.

[10] Agarwal, R and Garg, J K. (2007), Methane

Emission modeling using MODIS thermal and

optical data: A case study on Gujarat. Journal of the

Indian Society of Remote Sensing, 35 (4).

[11] Handbook of Statistics- Section B Directorate of

Rice Development. Source:

http://drd.dacnet.nic.in/HS-B-Table-13-

Andhra%20Pradesh.htm last accessed on 21st

February, 2013.

[12] Sheppard J C, Westberg H, Hopper J F, Ganesan K

and Zimmerman P, (1982), Inventory of Global

Methane Sources and their Production Rates.

Journal of Geophysical Research, 87, 1305-1312.

[13] Akumu C E, Pathirana S, Baban S and Bucher D

(2010), Modeling Methane Emission from

Wetlands in North-Eastern New South Wales,

Australia Using Landsat ETM+, Remote Sensing,

2, 1378-1399.

[14] Bhatia A., Pathak H and Aggarwal P K (2004),

Inventory of methane and nitrous oxide emissions

from agricultural soils of India and their global

warming potential. Current Science, 87(3), 317-

324.

[15] Cao M, Gregon K and Marshall S (1998), Global

Methane emissions from wetlands and its

sensitivity to climate change. Atmospheric

Environment 32(19), 3293-3299.

[16] Gupta P K, Gupta V, Sharma C, Das S N, Purkait

N, Adhyad T K, Pathak H, Ramesh R, Baruah K K,

Venkatratnam L, Singh G and Iyer C S P (2009)

Development of methane emission factors for

Indian paddy fields and estimation of national

methane budget. Chemosphere, 4(4), 590–598.

[17] Singh J S, Raghubanshi A S, Reddy V S, Singh S

and Kashyap A K (1998), Methane flux from

irrigated paddy and dry land rice fields and from

seasonally dry tropical forest and savanna soils of

India. Soil Biol. Biochem. 30(2), 135-139.

[18] Maik Netzband, Elizabeth .L.Wentz and Atiqur

Rehman (2012), Urban land cover and spatial

variation observation using satellite image data –

The urban environmental Monitoring Project;

Institute for International Sustainability, Arizona

State University, Tempe, AZ 85287 -211, USA

www.ursi.org/Proceedings/ProcGA05/pdf/F07.2(05

29).pdf (accessed on 20-Jan, 2012)

[19] Abrams M (2000), The Advanced Space borne

Thermal Emission and Reflection Radiometer

(ASTER): Data products for the high spatial

resolution imager on NASA’s Terra platform.

International Journal of Remote Sensing 21: 847-

859.

[20] Abduwasit Ghulam (2010), Calculating Surface

Temperature using Landsat Thermal Imagery,

Department of Earth & Atmospheric sciences, and

Centre of Environmental sciences, St. Louis

University, St. Louis, MO 63103.