PAVEMENT MANAGEMENT SYSTEM FOR

LOW VOLUME ROADS

Submitted in partial fulfilment of the requirements

of the degree of

Master of Technology

in Transportation Engineering

by

Koorma Rajendra Babu

Roll No. 111710

Supervisors:

Dr. Venkaiah Chowdary

Assistant Professor

Department of Civil Engineering

and

Dr. D.S.N.V. Amar Kumar

Assistant Professor

Department of Civil Engineering

Transportation Division

Department of Civil Engineering

NATIONAL INSTITUTE OF TECHNOLOGY, WARANGAL

AUGUST 2012

1. INTRODUCTION

1.1 General

Low Volume Roads (LVRs) constitutes an integral component of the road system in all

countries. Their importance extends to all aspects of the social and economic development of

rural communities. It is known that on a world-wide basis the number of low traffic roads far

exceeds the mileage of high traffic roads. India has a road network of over 4.32 million

kilometers in 2011, the third largest road network in the world. The LVRs in India forms a

substantial portion of the total road network. 3.11 million kilometers of road network was the

contribution of LVRs in India in 2011 which was 72% of the total road network length

(source: Indian Road Network, Wikipedia website). In view of this they often form the most

important link in terms of providing access to educational, medical, recreational and

commercial activities in local and regional areas. Universally, there is no exact definition for

Low Volume Roads (LVRs), but it could be defined primarily as a type of transportation

system typically constructed to manage or extract resources from rural or undeveloped areas.

These unique systems are designed to accommodate low traffic volumes with potentially

extreme axle loads. They are commonly defined as having less than 400 ADT (Average Daily

Traffic).

In the recent past, Government of India (GoI), with the initiation of the ambitious programme

Pradhan Mantri Gram Sadak Yojana (PMGSY) gave great importance to low volume roads

realizing their role in the economic development of rural areas. The vast expansion of the road

network has brought connectivity to the rural areas of the country. Majority of these roads are

constructed as flexible pavement with a thin bituminous surfacing layer of Open Graded

Premix Carpet (OGPC) of 20 mm as per the guidelines given in IRC:SP:20 (2002) and

IRC:SP:72 (2007). Current methods that are used for maintenance of these roads are based on

the judgment and experience of engineers, without considering the actual pavement

performance data. Regular maintenance would minimize not only wastage of financial

resources but also other resources such as equipment, manpower and materials. Earlier studies

reported that, the maintenance cost of a road which is in a very poor condition is four to five

times the cost if a pavement is regularly maintained while it is in a good condition (Haas et al.

1994). The allocation of budget towards maintenance of low volume roads are always on the

lower side, when compared with the high volume roads. Further the tendency is to neglect

those roads which are in low traffic volume and low composition of heavy commercial

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Vehicles. This has led to different procedures to be adopted for maintenance of LVRs and

therefore the allocated resources should be used wisely.

Pavement management has progressed from 1960’s to current widespread and successful

application in many countries around the world. And also there are “reinvention/invention”

needs involving succession planning, integration, adaption to privatization, longer lasting

pavements, performance models, quantification of benefits, incentive programs and very long

term life cycle analysis which have to be addressed if progress in pavement management is to

continue. According to Haas and Hudson (1978), “A Pavement Management System –

encompasses a wide spectrum of activities including the planning of programming of

investments, design, construction, maintenance and the periodic evaluation of performance.

The function of management at all levels involves comparing alternatives, coordinating

activities, making decisions and seeing that they are implemented in an efficient and

economical manner”. For effective PMS asset management is very important. Federal

Highway Administration (FHWA) and American Association of State Highway and

Transportation Officials (AASHTO) (1997) defined asset management as follows: “Asset

management is a systematic process of maintaining, upgrading and operating physical assets

cost-effectively. In the broadest sense, the assets of a transportation agency include physical

infrastructure such as pavements, bridges, and airports, as well as human resources (personal

and knowledge), equipment and materials, right-of-way, data, computer systems, methods,

technologies and partners”. With this background, in the present study, an effort is made to

develop Pavement Management System (PMS) for Low Volume Roads elevating the need of

Life Cycle Cost Analysis and Optimum Maintenance and Rehabilitation Strategy for the

same.

1.2 Low volume road scenario in India

During the year 1978, a working group was set up a planning commission for providing

information weather connectivity was established to all the villages of India (Planning

Commission, PEO, and Report No.:201, 2010). During the 1980s, Indian Road Congress

(IRC) conducted studies on the rural roads with the main objective to find out and quantify

the possible impact of roads on the socio economic development of rural areas. The survey

conducted in remote areas in India by Central Road Research Institute (CRRI) reported that

the villages located on the main road are comparatively well developed than those away from

the roads. These roads connecting different villages were made up of moorum or other locally

available granular materials of low quality standards and were built up by stage construction.

No pavement design procedure was adopted for construction of such roads. During the eighth

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five year plan (1992-97) priorities were accorded to link all villages with a population of 1000

and above to accelerate village connectivity.

Till the launching of PMGSY, the roads covered only 60% of villages/habitations in the

country. Understanding the various direct and in direct roles that low volume roads play, the

Government of India on 25th December, 2000 launched the PMGSY in order to provide road

connectivity, through good, all weather roads, to all rural habitation of targeted population.

At the commencement of PMGSY, it was estimated that about 3,30,000 habitations out of

8,25,000 habitations were without all-weather road connectivity, which means that about 40%

of the habitations were cut-off from the main stream development. Subsequently, it was

proposed to take up 1,73,000 unconnected habitations of population above 500 (250 in case of

hill, desert and tribal areas) under the PMGSY. Under the program 31,924 unconnected

habitations have been connected with the total constructed road length going up to 1,46,200

kilometers. About 1,55,000 kilometers existing rural roads have also been upgraded. It is

targeted to provide all weather connectivity to all habitations having a population of more

than 500 and more than 250 in hill states, tribal and desert areas by 2012.

1.3 Maintenance of low volume road

The development of low volume road received little attention in the various five year plans.

These plans largely concerned themselves with national highways or trunk roads. In 1967, a

special committee was set up to examine the status of rural roads and to suggest a suitable

development policy (11th five year plan working group on rural roads, 2006). This committee

perceived the crucial role of these roads in the context of speedy socioeconomic development

of the countryside. The fifth five year plan (1975/76 to 1979/80) envisaged a minimum needs

program for the villages, which contained road needs as one of its segments mandated that all

villages with a population of 1000 or more should have an all-weather road. In the seventh

five year plan document (1985-1990) the importance of low volume roads were constructed

under various rural road development programmes, which were mainly conceived for

employment generation and poverty alleviation. In such programmes serious efforts were not

made to build sustainable all weather roads with proper maintenance strategies (11 th plan,

GoI). It would not be out of recall that a World Bank study in 1988 demonstrated that

spending one rupee on road maintenance would have saved three rupees in rehabilitation.

In India, many of the low volume roads do not receive any funds for maintenance. Even if the

funds are available, various maintenance tasks and frequency is not clearly defined. Currently

the funds for rural roads are allocated based on the kilometer basis without much

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consideration of actual maintenance requirements which depends upon the traffic volume,

composition, terrain, environment, and drainage and subgrade condition. As a result the

system has started showing signs of premature failure. Huge funding is needed to bring the

system to desired serviceability level. In addition, low volume road agencies struggle with

limited staff, skills, equipment and information on pavement condition.

Although materials and techniques for pavement maintenance have improved, more research

is essential in the area of policy development for the effective management of pavement

maintenance, specifications development, cost-effective treatments and the best applications

of treatment timing. Many factors must be evaluated to select pavement maintenance

treatments. The factors may include cost of treatment, type, extent of distress, traffic volume,

climate, pavement type etc.

2. REVIEW OF LITERATURE

Kirori et al. (2001) suggested structure of the comprehensive road database for existing

flexible pavements to suit Indian highway engineers and provide inputs o maintenance work

planning modules under various budget scenarios. The dROAD (for road database) &dTIMS

(Total Infrastructure Management System for Database) software have been used. In this

study database mainly contains inventory, condition, traffic and strength perspectives with

140 data fields and 33 tables developed for coding. The roughness data have been collected

by axle mounted Bump Integrator and distress data by visual assessment. The structural

evaluation of pavement is presented in terms of Modified Structural Numbers (MSN) using

deflection-based relationships, though the relationship needs to be reviewed. The deterioration

models, Vehicle Operating Cost (VOC) equations have been adopted from different projects.

In various Pavement Management System (PMS) research/studies/projects, the condition

survey involved cumbersome exercise measuring cracked, raveled, potholed area, etc.

manually or visual condition rating and thus limited to smaller networks despite sincere and

precise works.PMS developed using comprehensive road database is based on ‘Life Cycle

Cost’ concept that includes construction cost, maintenance cost, rehabilitation cost and user

cost over the life of pavement. From the study conducted it was observed that Decision

Supporting System must be enhanced more to allocate budget on road network and there is a

need to standardize the classification of existing pavements on the basis of MSN with respect

to commercial vehicles per day.

Dhaliwal and Tipnis (2004) proposed the rehabilitation works for a disused airfield,

constituting a prestigious project, have been designed and planned by Military Engineers in

the recent past. Structural evaluation, functional evaluation and evaluation of existing 4

pavement facilities have been carried out despite of lack of information of pavement design

and construction records and the overlay design of the air field pavements was suggested in

this study based the condition of the pavement distress and severity of the distress type. The

construction alternatives that are considered during the air field pavement management are as

follows (i) complete removal of existing pavement and reconstruction of the pavements from

the subgrade, (ii) removal of badly damaged/failed pavement (approximately 35 % of existing

pavement), its reconstruction from the subgrade and re-strengthening overlay for the

remaining portion of airfield pavements, (iii) constructing of overlay for all airfield

pavements, utilizing the same as sub-base. Alternative (ii) was considered as most economical

solution considering initial construction cost and would suffice the design safety

considerations, if executed carefully. Alternative (i) would be obviously ideal solution, but

would have been very costly, though in long terms of life cycle costs, the alternative would

prove to be economical. Alternative (iii) would not have catered for the crucial safety aspects

and also would entail extensive and time consuming crack repairs, delaying the overlay

construction and thus lead to increase of the time related costs.

Aggarwal et al. (2004) developed a Pavement Management System for an identified National

Highway Network to assist the highway engineers responsible for maintaining the highway

network as well as the authorities responsible for allocating funds, in making consistent and

cost effective decisions, related to maintenance and rehabilitation of pavements. To predict

most economical maintenance strategy for a particular pavement section and prioritization of

maintenance activities in the event of a constrained budget. The pavement deterioration

models incorporated in HDM-4 have been calibrated to adapt to local conditions and suitable

calibration factors have been determined. The pavement deterioration models incorporated in

to HDM-4 have been calibrated using the pavement condition data collected on the pavement

sections and calibration factors for various deterioration models such as cracking, raveling,

potholing and roughness models have been obtained. Under project level PMS analysis, the

optimum Maintenance and Rehabilitation (M&R) strategy for a pavement section has been

determined on the basis of highest Net Present Value (NPV) / Cost ratio, amongst a number

of pre-defined M&R strategies. The average roughness value of the highway network keep on

increasing with gradual reduction in budget levels, which in turn may lead to very high road

user cost values.

Kandhal (2008) discussed the effective method for the repairing of the potholes in India. An

economical generic, readymade stockpile cold patching mix has been proposed as a part of the

experimental study. The generic mix can be placed without preparing the pothole, such as,

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drying, squaring the edges, cleaning and tack coating. The mix can be stockpiled and remains

workable for at least 6 months. The desirable characteristics of generic cold patching mix are

one-sized aggregate, use of least absorptive aggregate, adequate binder content and proper

amount of anti-stripping agent.

Bezabih and Chandra (2009) developed mathematical models to obtain Axle Load

Distribution (ALD) on a highway from its vehicle volume count. And ability to convert the

soil sub-grade strength given in terms of CBR in to modulus of subgrade reaction (k), and a

traffic load given in terms of Million Standard Axles (msa) in to ALD makes it possible to

design the two types of pavements for the same soil and traffic conditions. It was observed

from the models developed that flexible pavements show wider range of variation in cost with

respect to design parameters of traffic and soil CBR of which when compared to the rigid

pavements variation in cost is small. Flexible pavements are more economical for the lesser

volume of traffic.

Lee et al. (2009) developed a model equation to represent various pavement distresses such as

crack, rutting, roughness, etc. as one combined index in order to manage the pavement of

Korean asphalt national highway system at network level. Additionally, the threshold values

for rut depth and crack percent for resurfacing were determined to follow the

recommendations of pavement evaluation panel experts. The research method for the

development of NHPCI (National Highway Pavement Condition Index) involved selection of

the sample size and research subject area of pavement. Then, an automated road analyzer for

the investigation of pavement condition was used to measure the pavement condition

quantitatively. Afterwards panel experts qualitatively evaluated the measurement for a

multiple regression analysis. Moreover, the threshold value for pavement resurfacing was set

at 50% of the value recommended by the panel based on the rehabilitation strategies required

by the pavement evaluation card and the result of influential factors for the selection of

rehabilitation strategy. The study resulted in R2 value of 0.78 for the NHPCI model equation

developed in this study, and sensitivity analysis revealed that roughness (International

Roughness Index) and crack percent were sensitive. Finally, this study recommends average

rut depth value of 11 mm, crack percent of 20% as the threshold values for the pavement

resurfacing (overlay), and average rut depth value of 12 mm as threshold value for the

pavement mill-resurfacing (inlay).

Danial et al. (2010) used MicroPAVER software which is most comprehensive pavement

management software. Implementation of different budget scenarios provided in this software

such as: unlimited budget, annual budget, etc., as well as practical methods of maintenance

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and other unique features help the decision makers to successfully manage the pavements on

both network and project level by examining various trade-offs between allocated budgets. In

this study 10 main streets were studied and 131 pavement sections were investigated. The

average weighted condition for each branch, during a design period of five years, was

indicated to compare the effect of three different allocated budgets. Ten deterioration

prediction models were developed for the study network. Based on the average PCI for the

entire network the budget needed to maintain Weighted PCI for all branches was also

calculated using MicroPAVER software.

Qiang and Ling (2010) discussed the management and rehabilitation strategy of the pavement

network in china using the Discrete Optimization Techniques and proposed four discrete

optimization models which are formulated using integer programming with binary decision

variables. The objective function and constraints are based on the pavement performance and

prediction model using the Pavement Condition Index (PCI). Numerical experiments are

being in operation in Sichuan Province using four optimization techniques showing feasibility

and effectiveness of the proposed models.

Kobayashi et al. (2010) have proposed Deterioration forecasting modeling for an efficient

pavement management system. There is a problem for the accurate prediction of road

deterioration due to quality of pavement performance data and the different pavement

structural, material and environmental conditions. In this paper a methodology to estimate the

Markov Transition probability model is presented to forecast the deterioration process of road

sections. The deterioration states of the road sections are categorized in to several ranks and

the deterioration process are characterized by hazard models. The Markov transition

probabilities between the deterioration states which are defined by the non-uniform or

irregular intervals between the inspection points in time are described by the exponential

hazard models. In this paper, traffic-related data (ESAL) and Structural Number (SN) are

employed to estimate the exponential hazard model be expanded in various factors such as

temperature, pavement structure (type, layer thickness and modulus resilient of the sub grade)

and environmental variables.

Kirbas and Gursoy (2010) performed case study on developing a Pavement Management

System for the selected road sections. Pavement Management is in a broader sense, a working

program that involves all the procedures of planning, programming, designing, building,

maintaining and rehabilitation. A PMS arranges tools and methods to be used for determining

the best maintenance schedule for the decision makers in a given period. In this study

infrastructure distress data which is compatible with the distress identification manual

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published by American Society for Testing and Materials in 1999 is collected from 20

sections selected from the arterial road network in Besiktas district of Istanbul. The reasons

for the formation of distress types in the determined network are studied under the headings

like axle load, climate and other (material characteristics), and distress evaluations throughout

the whole network and each section are carried out. Paver system which is pavement

management system at several airports and cities/towns of USA and at different countries

which is developed during 1970’s by U.S. Army Corporation of Engineers. Paver system use

Pavement Condition Index (PCI) for determining current circumstances in pavement section.

Dattatraya (2011) presented the timely identification of undesirable distress in pavements at

network level using pavement management system. This paper summarizes the

implementation of a pavement condition prediction methodology using the Artificial Neural

Network (ANN) to forecast cracking, raveling, rutting and roughness for Low Volume Roads

(LVR) in India. Road Inventory data, as well six cycles of pavement performance data that

include distresses, subgrade characterization and traffic data, were collected from 61 in-

service LVR pavement sections over a 3 year period in India. ANN models with different

architectures were trained and tested to suggest the optimum ANN model. The study results

suggest that ANN models satisfactorily forecast future individual distresses. The performance

of the suggested ANN models is also compared to the calibrated HDM-4 models. The

suggested ANN models shows a high goodness of fit between observed distress and ANN

predicted distresses of more than a ratio of 0.98 for cracking, raveling, rut depth and

roughness progression models at the testing stage. The ANN models show a higher goodness

of fit regarding the predictability of distresses than that of HDM-4 calibrated distresses. This

is true for all four different ANN models, proving the success of ANN models over HDM-4

pavement deterioration models in predicting distresses. The suggested ANN models will be

useful in the accurate prediction of cracking, raveling, rut depth, and roughness. The models

can calculate the appropriate time for various maintenance strategies to preserve the huge

network of LVR in India and other developing countries with similar environmental and

traffic conditions.

Do (2011)produced methodology to estimate the mean life and failure probability in

consideration of road functional characteristics based on parametric and non-parametric

estimation models. Based on the three types of functionally classified roads: urban, rural and

recreation roads, five different lifetime distributions were tested: normal, lognormal,

exponential, weibull and loglogistic to select the appropriate probability distribution and to

estimate mean life and failure rates. The goodness-of-fit test, such as the anderson-darling

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test, was performed to select optimal probability distribution as a result of which approximate

distribution of each case was selected: lognormal distribution for rural roads and weibull

distribution for recreation roads. Non parametric estimation method for rural roads was

applied because there is no approximate probability distribution for rural roads. In this study

using probabilities it was found that mean life for urban, rural and recreation roads are about

6.8 years, 9.2 years and 9.5 years respectively for national highways in Korea.

Giustozzi et al. (2012) discussed the innovative methodology to evaluate the environmental

impact of preventive maintenance activities which relates the performance and cost during the

service life of the pavement through a multi-attribute (life cycle cost, performance and

environmental analysis). Performance deterioration models were used to identify the time

where preventive maintenance activities were needed based on pre-established threshold.

Three Pavement Management treatments that were considered were micro-surfacing, slurry

seal and thin overlay. Through this study it is also evident that less energy emission and CO2

during the process of operation of the maintenance strategy having eco-advantage.

Santos and Ferreira (2012) presents a new Life Cycle Cost Analysis (LCCA) system based on

an optimization model considering pavement performance, called OPTIPAV, developed and

programmed to help pavement designers to choose the best pavement structure for a road or

highway. LCCA system considers AASHTO serviceability concept. OPTIPAV program uses

minimization of the cost of maintenance of the pavement based on the Present Serviceability

Index (PSI) and adopts classified pavement structure based on the Structural Number (SN)

and Traffic Volume in terms of ESALs.

Chen et al. (2012) discusses the function design and the application effort of using GIS and

GPS in the system. The practice shows that the pavement inspection time is reduced greatly

because of the geospatial tools available for the maintenance and rehabilitation works and to

optimize pavement maintenance decisions. Pavement is divided in to sample units based on

the functional classification and individual sample units are examined thoroughly for the

measurement of distress and its extent to enter in to Geographical Information System(s).

From this study it is observed that using GIS leads to a multilevel analysis techniques for the

optimum management of Pavement Maintenance.

3. SUMMARY

All the developed distress prediction models should be subjected to measures of adequacy

before adoption and implementation in a Pavement Management System (PMS).Using of

Global Positioning System (GPS) when recording the distress data greatly increases the

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pavement management benefits. Pavement Condition Index methodology is used in many of

the developed countries for the pavement evaluation. Pavement Serviceability Index (PSI) and

Structural Number (SN) of the pavement are used as the parameters to reduce the

maintenance and rehabilitation costs. Life Cycle Cost Analysis (LCCA) of pavement

considers the serviceability concept adopted by the American Association of State Highway

and Transport Officials (AASHTO) for use in the design of flexible pavements. Discrete

optimization model uses PCI values as a main representative factor for the individual

pavement section units. A multi-attribute approach for life cycle assessment is needed to

evaluate the implications of incorporating the environment in to the decision making process,

in addition to cost and performance. Use of cold patching was adjudged as the best performer

in the readymade mix category in a nationwide field evaluation research project conducted

under the Strategic Highway Research Program (SHRP).The Markov transition probabilities

between the deterioration states, which are defined by the non-uniform or irregular intervals

between the inspection points in time, are described by the exponential hazard models. Based

on the deterioration rate the probability of failure of the pavement can be calculated using

which the suitable pavement maintenance and rehabilitation strategy was adopted. The road

network data collection in the field was divided under the following heads: (i) inventory data,

(ii) structural evaluation (structural capacity), (iii) functional evaluation (pavement condition

and riding quality) and (iv) evaluation of pavement materials. There should be an efficient

historical data base for developing of the regression models for pavement performance

prediction for kind of distresses. Including the Road User Cost Studies (RUCS) in the

development of PMS can lead to efficient Life Cycle Cost Analysis of pavement after the

maintenance and rehabilitation works. Road projects undertaken by any government agency

should be thoroughly verified by benefit to cost ratio because of socio-economic project

dispensing and to reduce frequency of road maintenance through various maintenance and

rehabilitation works.

4. REFERENCES

1) Aggarwal, S., Jain, S. S., and Parida, M. (2004). “Development of pavement management

system for Indian national highway network.” Journal of Indian Road Congress, 65(2),

271-326.

2) Bezabih, A. G., and Chandra, S. (2009). “Comparative study of flexible and rigid

pavements for different soil and traffic conditions.” Journal of Indian Road Congress, 70,

153-162.

3) Chen, W., Yuan, J., and Li, M. (2012). “Application of GIS/GPS in Shanghai airport

pavement management system.” International Workshop on Information and Electronics 10

Engineering (IWIEE),Heilongjiang, China,29, 2322-2326.

4) Danial, M., Ratnasamy, M., Hussain, B. H., and Zainuddin, B. M. Y. (2010). “Developing

a comprehensive pavement management system in tehran, iran using MicroPAVER.”

Electronic Journal of Geotechnical Engineering, 15, 1782-1792.

5) Dattatraya, T. T. (2011). “Artificial Neural Network (ANN) based pavement deterioration

models for low volume roads in India.” International Journal of Pavement Research and

Technology, 5(2), 115-120.

6) Dhaliwal, B. S., and Tipnis, M. M. (2004). “Planning for rehabilitation of a disused

airfield.” Journal of Indian Road Congress, 499, 203-218.

7) Do, M. (2011). “Comparative analysis on mean life reliability with functionally classified

pavement sections.” KSCE Journal of Civil Engineering, 15(2), 261-270.

8) Giustozzi, F., Crispino, M., and Flintsch, G. (2012). “Multi-attribute life cycle assessment

of preventive maintenance treatments on road pavements for achieving environmental

sustainability.” The International Journal of Life Cycle Assessment, 17, 409-419.

9) Haas, R., and Hudson, R. W. (1978). “Pavement Management Systems.” McGraw-Hill

Book Company, New York.

10) Haas, R., Hudson, W. R., and Zaniewski, J. (1994). “Modern Pavement Management.”

Krieger Publishing Company, Florida.

11) Indian Road Network, <http://en.wikipedia.org/wiki/Indian_road_network> (Aug. 24,

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12) IRC: SP: 20 (2002). “Rural Road Manual.” Indian Road Congress, New Delhi, India.

13) IRC: SP: 72 (2007). “Guidelines for the Design of Flexible Pavements for Low Volume

Rural Roads.” Indian Road Congress, New Delhi, India.

14) Kandhal, P. S. (2008). “A simple and effective method of repairing potholes in India.”

Journal of Indian Road Congress, 69(3), 189-203.

15) Kirbas, U., and Gursoy, M. (2010). “Developing the basics of pavement management

system in besiktas district and evaluation of the selection sections.” Scientific Research

and Essays, 5(8), 806-812.

16) Kirori, R. R. D., Singhvi, S. K., Swami, B. L., and Mina, H. L. (2001). “Development of

Pavement Management System (PMS) for national highways of NH division, PWD, Kota

(Rajasthan) using software dROAD & dTIMS.” Journal of Indian Road Congress, 64,

161-204.

17) Kobayashi, K., Do, M., and Han, D. (2010). “Estimation of markovian transition

probabilities for pavement deterioration forecasting.” KSCE Journal of Civil Engineering,

14(3), 343-351.

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18) Lee, J. S., Kwon, Y. C. S. S. A., Kim, G. Y., and Lim, K. S. (2009). “Development of

pavement condition index for Korean asphalt national highway and decision criteria for

resurfacing.” International Journal on Pavement Research and Technology, 2(3), 106-

114.

19) PMGSY Scheme, <www.pmgsy.nic.in> (Aug. 22, 2012).

20) Qiang, H. Z., and Ling, S. X. (2010). “Discrete optimization models and methods for

management systems of pavement maintenance and rehabilitation.” Journal of Shanghai

University, 14(3), 217-222.

21) Santos, J., and Ferreira, A. (2012). “Life-cycle cost analysis system for pavement

management.” Transportation Research Arena, Athens, Greece, 48, 331-340.

22) Srinivasan, R. C. (2010). “Evaluation study on rural roads component of bharat nirman.”

Report No. 210, Programme Evaluation Organization, Planning Commission, New Delhi.

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