increased application of labour-based methods unpaved rural roads by promoting appropriate...

Increased Application of Labour-Based MethodsThrough Appropriate Engineering Standards

Regional Report

Copyright © International Labour Organization 2006First published (2006)

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ILO

Increased application of labour-based methods through appropriate engineering standards.Regional reportHarare, International Labour Office, 2006

ISBN: 92-2-118861-2 and 978-92-2-118861-2 (print)

ISBN: 92-2-118862-0 and 978-92-2-118862-9 (web pdf)

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List of Tables iv

List of Figures v

Acknowledgements vii

Abbreviations viii

Executive Summary ix

1. Introduction 11.1 Background 11.2 Project objectives 21.3 Outputs 21.4 Reports 3

2. Scope of Study 52.1 Test site selection process 52.2 Selected test sites 62.3 Site commissioning 10

3. Test Site Details 113.1 Traffic 113.2 Rainfall 113.3 Road alignment 113.4 Material properties 11

4. Monitoring 154.1 Gravel loss 154.2 Roughness 154.3 Visual condition survey 17

5. Performance of the Labour-Based Constructed Roads 195.1 Gravel loss 19

5.1.1 Country-specific results 195.1.2 Combined results 22

5.2 Roughness 255.2.1 Country-specific results 255.2.2 Combined results 25

6. Comparison with HDM-4 Models 316.1 Gravel loss 316.2 Roughness 32

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Contents

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Increased Application of Labour-Based Methods Through Appropriate Engineering Standards

7. Life-Cycle Cost Methodology 37

8. Conclusions 41

9. Recommendations 43

Tables

Table 2.1: Climate classification based on TMI contours 6Table 2.2: Terrain classes 6Table 2.3: Ghana test sites 7Table 2.4: Uganda test sites 7Table 2.5: Zimbabwe test sites 8Table 2.6: Terrain and climate of test site locations 9Table 3.1: Traffic levels 11Table 3.2: Range of annual rainfall 11Table 3.3: Range of test site gradients 11Table 3.4: Material properties of the gravel wearing course 12Table 3.5: Material properties of the subgrade 12Table 4.1: Visual condition codes 18Table 5.1: Observed rates of gravel loss 20Table 5.2: Gravel loss trends 22Table 5.3: Gravel loss performance criteria 22Table 5.4: Representative rates of gravel loss 25Table 5.5: Observed average roughness 26Table 5.6: Observed rates of roughness progression 27Table 5.7: Roughness performance Criteria 28Table 5.8: Representative rates of roughness progression 30Table 6.1: Average country-specific gravel loss calibration factors 32Table 6.2: Representative gravel loss calibration factors 32Table 6.3: Representative roughness progression calibration factors 35Table 6.4: Representative material properties 35Table 6.5: Effect of grading on roughness 36

Figures

Figure 2.1 Test site selection process 5Figure 2.2 Plan view of peg layout on site 10Figure 3.1 Grading envelope for the gravel wearing course 13Figure 3.2 Grading envelopes for the subgrade 13Figure 4.1 MERLIN roughness measuring device 16Figure 4.2 Merlin probe assembly 17Figure 5.1 Rates of gravel loss in Ghana 19Figure 5.2 Rates of gravel loss in Uganda 21

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Figure 5.3 Rates of gravel loss in Zimbabwe 21Figure 5.4 Observed gravel loss by performance category 23Figure 5.5 Gravel loss performance categories vs material quality zones 24Figure 5.6 Cumulative frequency of standardised rates of gravel loss 24Figure 5.7 Observed rates of roughness progression by performance category 28Figure 5.8 Roughness performance categories vs material quality zones 29Figure 5.9 Cumulative frequency of standardised rates of roughness progression 29Figure 6.1 HDM-4 predicted rates of gravel loss 32Figure 6.2 Roughness progressions on unsealed roads with no maintenance 33Figure 7.1 Material quality zones 37Figure 7.2 Regravelling frequency example 38Figure 7.3 Grading frequency example 39Figure 7.4 Life-cycle cost example 40

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The work described in this report wasfunded by the Department forInternational Development (DFID), UK.

The work was carried out by theInternational Group of TRL Limited inpartnership with the International LabourOrganization/Advisory Support, Infor-mation Services and Training (ILO/ASIST).

The overall management of the project wasundertaken by Mr Tony Greening (TRLproject manager) in cooperation with MrDejene Sahle from ILO/ASIST. The TRLproject team comprised Dr Greg Morosiuk,Mr Kenneth Mukura and Mr Nick Elsworth.

The project team would like to acknowledgethe cooperation and assistance of theCentral Materials Laboratory (ChiefMaterials Engineer, Mr Ben Ssebbugga-Kimeze) for providing staff to undertake thefield work. The work was carried out underthe leadership of Mr Fred Were-Higenyi(project team leader) from the Ministry ofWorks, Housing and Communications.

The authors of this report are grateful to DrJohn Rolt (TRL), who carried out the qualityreview and auditing of the report.

Acknowledgements

ADT – Average daily traffic

CBR – California Bearing Ratio

DFID – Department for International Development

GL – Gravel Loss

GM – Grading Modulus

HDM – Highway Design Model

ILO – International Labour Organization

ILO/ASIST – International Labour Organization/Advisory Support, Information Servicesand Training

IRI – International Roughness Index

PP – Plasticity Product

TMI – Thornthwaite’s Moisture Index

TRL – Transport Research Laboratory, UK

vpd – vehicles per day

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Abbreviations

Road access to health centres, schools,jobs, etc. is an important factor in thesocial and economic development of

rural communities in Africa. Most roadsproviding access to small towns and villagestend to be unsealed and constructed ofearth or gravel. Climatic and environmentalinfluences can be dominant factors in thedeterioration of these roads and their life-time performance is also influenced byfactors such as terrain and constructionmaterials, as well as traffic. Access, throughreduced trafficability and passability, isoften severely curtailed in the wet season.With unpaved roads typically comprising70-80% of road networks in Africa, theinvestment in these roads represent aconsiderable proportion of the asset valueof the total road network.

At the Extraordinary African Heads of Statesmeeting held in October 2004 inOuagadougou, Burkina Faso, to discussemployment and poverty reduction issues,the heads of states made a commitment touse employment-friendly approaches in allsectors that have employment creationpotential. Infrastructure, especially the roadsectors is one that has a high potential forcreating employment provided theappropriate choice of technology is made.

The roads are seldom designed, let aloneproperly appraised. Where appraisals havebeen carried out, the costing usually onlycovers the planning and construction stages.This information is normally obtained fromthe Bill of Quantities, which in some cases isnot produced. Many roads are constructedby labour-based technology, which canfurther restrict haulage and access to goodroad building materials, more-so insubsequent regravelling activities. The

consequences in qualitative terms, from theuse of inferior materials such as ravellingand/or slipperiness, are well known andcurrent specifications cover these aspectsadequately. However, their performance interms of gravel loss and roughnessprogression, which determine their verysustenance and the resultant life-cycle costs,are less known. In addition, the existingstandards and specifications are based onstructural requirements rather thanperformance. This has had a huge impact onthe sustainability of rural and peri-urbanroads. It is interesting to see how in somecases roads that mushroom through somewell-intended programmes quickly dis-appear not long after they are completed.The inadequate budgets for the manage-ment of road networks have left responsibleagents strapped for funds for the main-tenance of existing roads, let alonesustenance for developing new roads. Someregions are posting a net loss in rural roadinfrastructure as a result.

The goal of this project was ‘To promotesustainable livelihoods and contribute tothe socio-economic development of dis-advantaged rural populations through theprovision of improved road access’. Thepurpose was ‘To reduce the life-time costsof unpaved rural roads by promotingappropriate engineering standards,planning tools and works procedures forlabour-based construction and main-tenance’. The factors influencingdeterioration of unpaved roads and theireffect on the overall performance of theroads have been determined in this project.To achieve this, test sites were selected andestablished in Ghana, Uganda andZimbabwe and parameters such as materialproperties, traffic, environment, etc. were

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Executive Summary

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studied over a 2-year period. A detailedanalysis was carried out and the resultswere interpreted into useable informationin the form of new performance-basedstandards and specifications and a usefullife-cycle costing software tool. This hasresulted in increased opportunities forbetter and more robust engineering designsfor rural unpaved roads. The life-cyclecosting tool will be useful to decision-makers, planners, roads authorities, anddonors alike, as it can be used as aninvestment tool. Appropriate decisions canbe made and justified from the outset thatwill ultimately culminate in sustainable ruralroad networks.

Improved access significantly contributes tothe well-being of communities living in thearea. It is for this reason that there is anincreased demand for access roads. As thedemand for access is high, it is essential toincrease the use of locally availableresources and cost-efficient methods ofdelivery. Low volume trafficked roads aresuitable for the use of appropriate tech-nology that promotes local resources. Thisapproach will speed up local economicdevelopment and intensify the fight againstpoverty. It is for these reasons most nationaldevelopment plans have included employ-ment as central to the improvement andmaintenance of infrastructure.

Appropriate standards and specificationsalone are not enough to guarantee goodperformance of roads. Much also hinges onthe quality standards employed in theapproval of materials and works. This issuehas been addressed in this project throughthe development of a quality assuranceGuideline entitled ‘Guideline for Quality

Assurance Procedures and Specifications forRoad Works Executed using Labour-BasedMethods’. The Guideline will assist inquality control and workmanship on labour-based projects. This in turn will ensure valuefor money for clients and rapid approval ofworks carried out by contractors. Above all,this will lead to the production of goodquality products, the impact of which will bean increase in the application of the labour-based technology.

The objectives of the project have beenlargely met. The deterioration relationshipsand the associate life-cycle costs tool thathave been developed, the review ofengineering standards and specificationsand the quality assurance guideline will allassist in promoting labour-basedtechnology to a new level where it can beviewed as a more competitive option. Theprovision of good quality roads usinglabour-based methods through the use ofappropriate standards and specificationsand tools will contribute to sustained ruraland peri-urban development, therebyimpacting positively on poverty. This willfulfil the goal of this project.

Notwithstanding the positive results of thisproject, the full benefits will not be realisedunless these research findings and outputare implemented to enhance best practicein the application of labour-based methods.It is therefore recommended that acomprehensive programme for awareness,dissemination, training, trialling and main-streaming be formulated and executed. Thiswill result in the efficient use of resourcescurrently earmarked for labour-based worksin various countries in Africa and beyond.

1. Introduction

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1.1 Background

Low-volume earth and gravel roads areoften constructed by labour-basedmethods using quite different

construction techniques and lighterequipment than used on projectsconstructed by conventional methods.Quantitative information on theengineering performance and modes ofdeterioration of these types of road islimited and thus it is difficult to setappropriate standards or to know the effectof different standards on their performance.This means that the expected level ofmaintenance is difficult to ascertain, andwhole-life costs and benefits are almostimpossible to determine.

In addition, most road authorities andagencies have noted concerns over thehuge burden associated with sustainingrural unpaved roads in general. Whilstsome roads may last a long time, othershave very short re-gravelling cycles (2 to 3years). Under such circumstances the cost ofsustaining such networks exceeds thecapacity of responsible authorities, resultingin the poor state of roads. With increasedawareness, road users are demandingbetter services in line with levies that theypay for road provision and as such, goodriding quality and low vehicle operatingcosts are now an issue. To improve thecondition of roads, many authorities haveadopted performance-based maintenancetechniques in which the quality of roaddetermines the work that needs to be doneto maintain the desirable standards.

It is therefore imperative that factorsinfluencing deterioration and maintenancedemands are considered and investigated

in order that mitigating measures are put inplace through engineering processes andstandards. To achieve this objective,currents standards and specifications, whichare more inclined towards structuralstrength, needed reviewing in order todevelop new standards that are based onboth performance and structural strength. Itis also acknowledged that performance ismainly dependent on the quality ofmaterials used, the workmanship, theappropriateness of the piece of infra-structure to its intended functionality andthe influencing environment. A measure ofperformance is paramount to the wholeprocess, and in the case of unpaved roadsthe performance is commonly measured bythe rate of gravel loss and rate of roughnessprogression.

Therefore, quantitative information on themodes of deterioration is required fordifferent types of very low-volume roads sothat appropriate engineering standards canbe set, methods to monitor compliance withstandards developed and proceduresdetermined that enable life-cycle costs to becalculated.

The importance of well-defined standardshas become particularly evident, with thecurrent emphasis on private sectorinvolvement in low-volume road projectsthrough the training and engagement ofsmall-scale contractors. Construction ofroads, whether highly trafficked trunk roadsor village tracks, by the private sector isconducted as a business and, as with anybusiness, reducing costs increases profits.Whilst measures that reduce costs throughincreased efficiency are to be encouraged,those which result in poor quality are notacceptable. Without setting appropriate

standards and methods to monitorcompliance with these standards, it isimpossible to check on the quality of roadsbeing constructed.

Labour-based methods and other lessconventional techniques are used toconstruct a wide variety of roads, which varyfrom tracks providing basic access to highlytrafficked gravel roads and, occasionally,even sealed roads. On lightly traffickedroads, construction standards will to someextent depend on the equipment available.Deterioration due to environmental andclimatic effects on these roads can begreater than that due to traffic. This is theimportant difference between these andmore highly trafficked roads.

The outcome of this project is expected toculminate in a number of benefits for roadagencies and authorities, the peri-urbanand rural communities:

1. Use of appropriate standards andspecifications will result in moresustainable and manageable roadnetworks, thus reducing the main-tenance burden and increasing theopportunities for the expansion of roadnetworks and improved accessibility.

2. The use of performance based designswill enhance durability of the roads,thereby reducing the demand for gravelextraction and environmentaldegradation.

3. More appropriate decisions will bemade at planning and design stages inorder to ensure quality and per-formance. This will help to reduce themaintenance burden, which is a majorissue for authorities and agenciesresponsible for low-volume roads asfunding is almost always limited.

4. The information emanating from theresearch is in itself an important tool forthe decision-makers in determiningappropriate road types for differentenvironments.

5. The life-cycle costing methodology willalso be a useful tool, adding a newdimension incorporating economic

considerations as an integral part oflow-volume roads provision. This will bea useful negotiating tool not only forproject appraisal but for solicitingfunding from donors and otherfinanciers.

6. Communities, especially the margin-alised populace, will benefit immenselyfrom improved transport servicesresulting from increased sustainability oflow-volume road networks.

7. Perhaps the highest beneficiary will belabour-based technology itself. Im-proved standards and quality will resultin greater appreciation of labour-basedtechnology and this is likely to improveits competitiveness, acceptability andreliability.

1.2 Project objectives

The project goal is to promote sustainablelivelihoods and contribute to the socio-economic development of disadvantagedrural populations through the provision ofimproved road access.

The purpose of the project is to reduce thelife-time costs of unpaved rural roads bypromoting appropriate engineeringstandards, planning tools and worksprocedures for labour-based constructionand maintenance.

This project has been carried out inpartnership with the International LabourOrganisation/Advisory Support, InformationServices and Training (ILO/ASIST).

1.3 Outputs

The main outputs of the project are:

a) Deterioration relationships establishedfor low-volume unpaved roads.

b) Methodologies developed and docu-mented for determining life-cycle costsof labour-based roads.

c) Appropriate engineering standardsdeveloped and guidelines produced fordifferent categories of labour-basedroads in different environments.

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d) Appropriate methods established andguidelines produced for qualityapproval of labour-based constructionand maintenance works.

e) Results disseminated to traininginstitutions, relevant ministries andsmall-scale contractors associations.

The outputs of the project will contribute toincreasing awareness by road authoritiesand other stakeholders, such as policy- anddecision-makers, communities, professionalbodies, etc. of the potential benefits ofusing optimised labour-based roadtechnology and increase the applicability oflocal resource use.

1.4 Reports

Three studies investigating the performanceand standards of roads that are constructedby labour-based methods were carried outin Ghana, Uganda and Zimbabwe.Individual country reports were producedwith recommendations and guidance basedon that country only. This report synthesisesthe findings from the three countries andprovides guidelines for countries withsimilar terrain, climate and materials.

The report has been produced to give aregional perspective to the findings forapplications by a wider spectrum ofpractitioners in different countries. It shouldbe noted though that the programme ofwork is expanding as more countriescontinue to join in and participate in theresearch. The results and outcomes willcontinue to be improved as moreinformation becomes available from on-

going projects and others that have yet tostart. Technically and statistically, resultsand outcomes of research become morereliable with increased amounts of data. Itshould be noted, however, that the resultsof this research have produced very usefultools which are documented in this andother reports.

In addition to this report, a manual entitled‘Guideline for Quality Assurance Pro-cedures and Specifications for Road WorksExecuted Using Labour-Based Methods’ hasbeen produced covering

1. Standards and specifications for theselection of materials.

2. Standards and specifications for theapproval of works.

3. Quality assurance through planningand design.

4. Economic appraisal through the life-cycle costing tool (software programme).

The guideline is a useful document forlabour-based practitioners and also non-labour-based practitioners, providingthe means to ensure that good quality roadsare built, thereby enhancing value formoney for the clients. In addition, the qualityof labour-based works has been acontentious issue, creating a barrier to thedevelopment and promotion of thetechnology. This guideline will help topromote the use of labour-based technologyand this will in turn help fulfil one of themajor objectives of this project which is the‘increased application of labour-basedtechniques and whole-life costing for lowvolume labour-based and unpaved roads’.

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2.1 Test site selection process

The objective of the test site selectionprocess was to select lengths of roadthat had been constructed using

labour-based techniques that covered awide spectrum of factors, primarily traffic,construction materials and climate. The siteselection approach adapted in this study isshown in Figure 2.1.

Desk study

At this stage available information, dataand local experiences were collected andcoordinated. This process helps to map apicture of the local situation and develop astrategy for implementation of the project.The desk study was carried out on the basis

of a research matrix which encompassedmaterial properties, traffic, terrain, roadgeometry, climate and construction (ageand standards). The range for each of theseparameters in any one country is usuallylimited, hence the project took a regionalperspective to cover wider ranges. Possiblecandidate roads were considered at thisstage, focusing on the need to cover asmuch of the elements of the matrix aspossible.

Field reconnaissance

Once the desk study was completed andpossible locations of sites identified, theauthorities responsible for these roads wererequested to select candidate roads withspecific emphasis on the particular aspects

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2. Scope of Study

Figure 2.1Test site selection process

of the matrix as depicted by the desk study.This process also identified potentialduplicate locations. Field visit were made tothe selected roads and this marked thebeginning of site selection.

Preliminary site selection

Preliminary site selection was carried outbased on the research matrix, primarilytraffic levels, road alignment, climate andthe gravel wearing course material.Material properties constitute the mostimportant part of the research and visualassessments alone are not adequate. Forthis reason samples were collected fortesting.

Sampling

Samples of wearing course were collectedfrom each site. Usually, one sample wascollected from the centre of thecarriageway. In some cases two sampleswere collected and results would then beaveraged. Care was taken to avoidcontamination of the wearing course andthe underlying subgrade. Also it wasnecessary to ensure that the sample was bigenough to carry out sieve analysis anddetermine Atterburg limits for plasticity. Insome cases proctor tests and CBR weretaken into consideration. During samplingthe depth of wearing course was measured– this was also a useful parameter forselection. Sites with less than a 50mmwearing course thickness were rejected asthis was not enough to last through themonitoring period.

Final selection

All the data were collated and analysed,and the results were used to produce a finallist of sites for the project.

Thornthwaite’s Moisture Index (TMI) wasconsidered a suitable measure to indicateclimate in a region. Climatic boundaries interms of TMI encountered in the threecountries have been classified as shown inTable 2.1.

Table 2.1: Climate classificationbased on TMI contours

TMI Range Climate Classification

+20 to 0 Moist sub-humid

0 to -20 Dry sub-humid

-20 to -40 Semi-arid

Terrain in which the test sites were situatedwas classified as either flat, rolling ormountainous, as described in Table 2.2.

Table 2.2: Terrain classes

Class Description

Flat 0 – 10 five metre ground contours per km

Rolling 11 – 25 five metre ground contours per km

Mountainous > 25 five metre ground contours per km

Soil samples were collected from these roadsto determine their classification, plasticity andgrading. The results of these tests were usedto eliminate duplication of similar roads aspossible locations for the test sites.

2.2 Selected test sites

A total of 63 test sites were selected in thethree countries, 24 sites in Ghana, 8 sites inUganda and 31 sites in Zimbabwe, asshown in Table 2.3, Table 2.4 and Table 2.5respectively. For easy referencing, the siteswere referred to by a code consisting of fourletters, the first and last letter of the twoplaces connected by the road. For examplethe test site on the NahA-LoggU road wasreferred to as NALU.

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Table 2.3: Ghana test sites

No. Road Name Site Code Climate Material

1 Assin Ayaasi – Assin Kruwa AIAA Moist sub-humid Fine lateritic gravel

2 Abrewanko J'tn – Abrewanko ANAO Dry sub-humid to semi-arid Lateritic gravel

3 Ayomso – Awia Awia AOAA Dry sub-humid Brown lateritic gravel

4 Asuotiano – Dorma Akwamu AODU Dry sub-humid Stoney gravel

5 Asawinso – Kojokrom AOKM Moist sub-humid Fine lateritic gravel

6 Bulenga – Chaggu BACU Semi-arid Fine lateritic gravel

7 Bonakye – Potimbo BEPO Semi-arid

8 Bechem – Bremie BMBE Dry sub-humid Brown coarse laterite

9 Bonzain J'tn – Bonzain BNBN Moist sub-humid Fine lateritic gravel

10 Bodi J'tn – Nsawura BNNA Humid Coarse lateritic gravel

11 Dimala – Bongnayili DABI Semi-arid

12 Duayaw Nkwanta – Camposo DACO Dry sub-humid Brown lateritic gravel

13 Dominanse – Wrakese S'tn DEWN Moist sub-humid Fine lateritic gravel

14 Datano – Kokooso DOKO Moist sub-humid Fine lateritic gravel

15 Forifori – Dwamena FIDA Semi-arid

16 Grumani J'tn – Kpachiyili GNKI Semi-arid

17 Linso – Nframakrom LONM Sub-humid Sandy gravel

18 Naha – Loggu NALU Semi-arid Lateritic gravel

19 Nabogu – Sung NUSG Semi-arid

20 Pong Tamale – Yapalsi PEYI Semi-arid

21 Samanhyia – Kyemfre SAKE Dry sub-humid to semi-arid

22 Wiaga – Kpalansa WAKA Semi-arid Lateritic gravel

23 Wamfie – Praprababida WEPA Dry sub-humid Brown laterite gravel

24 Zebilla – Timunde ZATE Semi-arid Lateritic gravel

Table 2.4: Uganda test sites

No. Road Name Site Code Climate Material

1 Kisanja – Paraa KAPA Semi-arid Brown clayey gravel

2 Kisoro – Muganza KOMA Wet humid to sub-tropical Dark brown sandy clay

3 Muhokya – Mahango MAMO Wet humid to sub-tropical Dark grey volcanic ash

4 Mparo – Kibugubya MOKA Semi-arid Dark grey clayey soil

5 Molo – Kidoko MOKO Wet humid to sub-tropical Brown quarzitic gravel

6 Nakaloke – Kabwangasi NEKI Wet humid to sub-tropical Black cotton soil

7 Nankusi – Bumudu NIBU Wet humid to sub-tropical Dark red clayey gravel

8 Rubona – Kabuzige RAKE Wet humid to sub-tropical Brown sandy clayey gravel

The terrain in which the test sites werelocated, together with the climate, areshown in Table 2.6.

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Table 2.5: Zimbabwe test sites

Road Name No. Site Code Climate Material

Chikuku – Makuwaza 1 CUMA 1 Dry sub-humid Quartz2 CUMA 2 Dry sub-humid Quartz

3 KAME 1 Dry sub-humid SandstoneKatarira – Mahuwe 4 KAME 2 Dry sub-humid Sandstone

5 KAME 3 Dry sub-humid Sandstone

Maranda – Mwenezi 6 MAMI 1 Semi-arid Calcrete7 MAMI 2 Semi-arid Calcrete

8 MEME 1 Semi-arid LateriteMkwasine – Matsange 9 MEME 2 Semi-arid Laterite

10 MEME 3 Semi-arid Laterite

Mutoko – Nyamuzuwe 11 MONE 1 Dry sub-humid Quartz12 MONE 2 Dry sub-humid Quartz

Mpoengs – Maphisa 13 MSMA 1 Semi-arid Calcrete14 MSMA 2 Semi-arid Calcrete

Nyamaropa – Chiso 15 NACO 1 Dry sub-humid Quartz16 NACO 2 Dry sub-humid Quartz

Nyanga – Rwenya 17 NARA 1 Dry sub-humid Quartz18 NARA 2 Dry sub-humid Quartz

Nyafaru – Katiyo 19 NUKO 1 Moist sub-humid Quartz + Feldspar20 NUKO 2 Moist sub-humid Quartz + Feldspar

Plumtree – Somnene 21 PESE 1 Dry sub-humid Quartz22 PESE 2 Dry sub-humid Quartz

Suswe – Chitsungo 23 SECO 1 Dry sub-humid Iron oxide, Quartz24 SECO 2 Dry sub-humid Iron oxide, Quartz

Sarahuru – Maranda 25 SUMA 1 Dry sub-humid Feldspar26 SUMA 2 Dry sub-humid Feldspar

27 TEPU 1 Dry sub-humid Quartz + D. GraniteTinde – Pashu 28 TEPU 2 Dry sub-humid Quartz

29 TEPU 3 Dry sub-humid Quartz + D. Granite

Tsholotsho – Sihazela 30 TOSA 1 Dry sub-humid Calcrete31 TOSA 2 Dry sub-humid Calcrete

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Table 2.6: Terrain and climate of test site locations

TerrainClimate

Semi-arid Dry Sub-humid Moist Sub-humid

Flat Ghana BACU Ghana BMBE Ghana LONMBEPO WEPADABI Zimbabwe NUKO 1FIDA Zimbabwe KAME 2 NUKO 2GNKI KAME 3NALU NARA 2 Uganda NEKINUSG PESE 1PEYI PESE 2

WAKA SUMA 1ZATE SUMA 2

TEPU 1Zimbabwe MAMI 1 TEPU 2

MAMI 2 TEPU 3MEME 1 TOSA 1MEME 2 TOSA 2MEME 3MSMA 1MSMA 2

Uganda KAPA

Rolling Uganda MOKA Ghana ANOA Ghana AIAAAOAA AOKMAODU BNBNDACO BNNASAKE DEWN

DOKOZimbabwe CUMA 1 Uganda KOMA

CUMA 2 MOKOKAME 1NACO 1NACO 2NARA 1SECO 1

Mountainous Zimbabwe MONE 1 Uganda MAMOMONE 2 NIBUSECO 2 RAKE

2.3 Site commissioning

The sites were commissioned byestablishing steel pegs at 20 m intervals onboth sides of the road over the 200 mlength of the site. These steel pegs werethen used as fixed references for measuringgravel loss. Figure 2.2 shows the typicallayout of the pegs.

After concreting the pegs, they were surveyedwith an automatic level and staff to establishtheir relative positions or reduced levels in

relation to the benchmarks. The survey wasclosed back in order to ensure that therequired accuracy was achieved. A toleranceof 6 mm was allowable for this survey,referred to as the peg survey. Reduced levelswere calculated and recorded.

Surveys of the change in the road profilewere taken between the benchmarks. Thiswas carried out by pulling a measuring tapeacross to road between two pegs at eachcross-section and taking levels at 20 cmintervals.

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Figure 2.2Plan view of peg layout on site

3.1 Traffic

Classified traffic count surveys werecarried out on the test sites. Theranges of traffic levels (Average Daily

Traffic – ADT) observed on the sites in eachcountry are shown in Table 3.1. Also shownin Table 3.1 are the average compositionsof light vehicles (< 3.5 tonnes) to heavyvehicles (> 3.5 tonnes) observed on thesesites in each country.

Table 3.1: Traffic levels

Average Daily Traffic % Light % HeavyCountry (ADT) Vehicles Vehicles

Min Max Average ADL ADH

Ghana 3 462 127 62% 38%

Uganda 4 226 58 82% 18%

Zimbabwe 4 140 38 74% 26%

3.2 Rainfall

Data from the rainfall stations locatednearest each test site were collected fromthe country’s meteorological office andassigned as the rainfall for that site. Theranges of annual rainfall recorded for thesites in each country are shown in Table 3.2.

Table 3.2: Range of annual rainfall

CountryRainfall (mm/year)

Min Max Average

Ghana 539 1485 1135

Uganda 462 2231 913

Zimbabwe 731 1415 1010

3.3 Road alignment

The gradient of each site was measuredusing a rod and level. The ranges of thevertical alignment on the sites in eachcountry are shown in Table 3.3.

Table 3.3: Range of test sitegradients

CountryGradient (m/km)

Min Max Average

Ghana 1.0 23.7 11.6

Uganda 11.9 47.8 27.3

Zimbabwe 0.9 67.1 17.9

3.4 Material properties

Samples of the gravel wearing course andthe subgrade were taken for materialtesting from the centre of the carriagewayat locations that were immediately adjacentto each of the 200 m test sites. Tests carriedout on the samples included gradinganalysis, Atterberg and shrinkage limits.The results of the tests on the gravelwearing course are summarised in Table3.4 and on the subgrade material aresummarised in Table 3.5.

The grading envelopes from the samples ofthe gravel wearing course from the threecountries are illustrated in Figure 3.1 andfor the samples of the subgrade areillustrated in Figure 3.2.

11

3. Test Site Details

12


Table 3.4: Material properties of the gravel wearing course

Material PropertyGhana Uganda Zimbabwe

Range Average Range Average Range Average

Liquid Limit WL 21 - 48 32.0 29 - 55 45.1 19 - 47 30.0

Plastic Limit PL 12 - 26 17.9 0 - 30 18.8 13 – 30 18.2

Plasticity Index Ip 4 - 22 13.9 17 - 44 26.4 0 - 27 8.0

Plasticity Modulus PM 136 - 1001 498 352 - 1431 786 0 - 1134 331

Plasticity Product PP 77 - 740 310 44 - 1032 551 0 - 756 211

Plasticity Factor PF 197 - 751 386 0 - 1075 479 0 - 1032 311

Dust Ratio DR 0.31 - 0.90 0.59 0.13 - 0.87 0.63 0.22 - 0.69 0.50

Coarseness Index Ic 12 – 71 48 33 - 75 51 1 - 59 32

Grading Modulus GM 1.2 - 2.3 1.9 1.4 - 2.4 2.0 1.0 - 2.2 1.7

Grading Coefficient Gc 6.0 - 20.6 15.5 3.1 - 20.5 14.5 0.4 - 17.5 10.0

Table 3.5: Material properties of the subgrade

Material PropertyGhana Uganda Zimbabwe

Range Average Range Average Range Average

Liquid Limit WL 18 – 55 27.4 40 – 84 50.3 16 - 48 27.7

Plastic Limit PL 8 – 32 15.1 19 - 51 26.6 13 - 29 18.5

Plasticity Index Ip 5 – 23 12.3 16 - 33 23.6 0 - 20 5.0

Plasticity Modulus PM 218 – 1544 696 320 - 2508 1626 0 - 1590 330

Plasticity Product PP 117 – 1136 435 160 - 2376 1232 0 - 1200 190

Plasticity Factor PF 154 – 1615 536 290 - 3672 1403 0 - 1680 304

Dust Ratio DR 0.27 – 0.82 0.59 0.50 – 0.95 0.72 0.21 – 0.75 0.47

Coarseness Index Ic 3 - 55 26.1 0 - 55 18.8 0 - 59 17.3

Grading Modulus GM 0.5 - 2.1 1.4 0.3 - 2.3 1.0 0.6 - 2.0 1.3

Grading Coefficient Gc 2.3 - 19.6 12.2 0 - 12.5 8.4 0 – 17.3 8.0

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Figure 3.1Grading envelope for the gravel wearing course

Figure 3.2Grading envelopes for the subgrade

The test sites were regularly monitoredfor a period of approximately 2 years.In Ghana the monitoring commenced

in March 2002, in Uganda in May 2002 andin Zimbabwe in October 2001. The sites inGhana were monitored a total of four times,in Uganda seven times and in Zimbabwethree times.

The following surveys were conductedduring each monitoring site visit:

a. Gravel loss measurements.b. Roughness measurements.c. Visual condition survey.

Most of the survey work was carried out bylocal teams as part of their contribution totheir respective project. In addition, the useof local personnel fitted well with one of themain principles of the project of transferringknowledge and research technology to localpractitioners through practical involvement.Initially, the team members were trained onhow to conduct the surveys. In most cases,levelling was carried out by trainedsurveyors and materials testing by qualifiedmaterials technicians. Mentorship wasprovided until adequate hands-onexperience was attained. Field forms wereproduced by TRL and the same forms wereused in all the participating countries.

Visual condition surveys proved to be moredifficult because of the subjective natureassociated with the assessment being doneby different individuals in different countrieswith different perspectives. The guide thatwas provided on the parameters andmethodology of assessment helped thereduce subjectivity but did not eliminate it.

4.1 Gravel loss

Gravel loss was estimated by monitoringcross-section profiles of the road betweeneach pair of pegs, i.e. every 20 m along thetest site. At each cross-section, the spotheight was measured at 20 cm intervals(called offsets) across the carriageway usinga rod and level. The 20 cm intervals wereidentified using a measuring tape heldtightly across the carriageway between apair of pegs. The spot heights were thenreferenced to the benchmark readings.

Before measuring the cross-section profiles,it was important to check whether the pegshad moved, as movement of the pegswould significantly affect the profile and theestimated gravel thickness/loss. The heightof each peg was therefore checked againstthe original survey records at the start ofeach survey and any movement taken intoaccount when comparing the reduced levelsbetween surveys.

The width of the carriageway wasdetermined at each cross-section on a testsite and the average of the reduced levelsacross the defined width was used toestimate the height of the gravel wearing-course at each cross-section. The samedefined width at a cross-section was usedthroughout the monitoring period. Thechange in the average height of thecarriageway between surveys was used asthe indicator of the change in gravel loss.

4.2 Roughness

Roughness is a measure of the riding qualityof the surface and can be measured using a

15

4. Monitoring

variety of instruments. Whicheverinstrument is used, it is important that themeasurements are standardised in theuniversally accepted units of InternationalRoughness Index (IRI). A relativelyinexpensive roughness measuring device isthe Merlin (see Figure 4.1) and it was usedto measure roughness on the test sites. Themeasurements from the Merlin can bestandardised to IRI units. The Merlin’soperation is detailed in the Test SiteSelection, Commissioning and Monitoringreport.

The Merlin can be operated in one of twodifferent modes based on the location of themeasuring foot shown in Figure 4.2. Bychanging the position of the foot themagnification factor can be set to either 5:1(for rough surfaces) or 10:1 (for smoothsurfaces), indicating how far the chartpointer moves compared to themeasurement probe. For the unsealedlabour-based sites, a magnification of 5:1was used. Prior to use, the Merlin has to becalibrated to correct any discrepancy in themagnification between the probe and thechart pointer.

The number of Merlin measurements alongthe site (in each wheelpath) should beapproximately 200 to ensure that the dataare representative of the site. Themeasurement interval is usually determinedby the circumference of the Merlin wheel,i.e. the distance along the ground travelledby one rotation of the wheel, which isapproximately 2.1 m. Hence for the 200 mlong test sites, it was necessary for areading to be made every half revolution ofthe Merlin wheel, which meant thatapproximately 190 readings were made ineach wheelpath.

16


Figure 4.1MERLIN roughness measuring device

The measure of spread of 90% of the Merlinreadings (i.e. 5% of readings from eitherend of the distribution are ignored) isreferred to as ‘D’. The roughness, in termsof IRI units, was then evaluated using therelationship:

IRI = 0.593 + (0.0471 x D)

4.3 Visual condition survey

Each 200 m test site was divided into 20 msub-sections with the pegs forming theboundaries. For each 20 m sub-section, thesurface condition was recorded on a datasheet, as shown in Appendix B, by asurveyor/technician who walked along theroad. The parameters that were recordedare listed in Table 4.1, with the drain andshoulder information collected separatelyfor both the left and right side of the road.

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Figure 4.2Merlin probe assembly

18


Table 4.1: Visual condition codes

Parameter Ranges

Drain Drainage Very Good, Good, Average, Poor, Very Poor

Drain existence Exists, Not required, Required

Scouring None, Slight, Severe

Blockage None, Slight, Severe

Shoulder Side slope condition No damage, Moderate, Badly Damaged

Side slope damaged Area damaged in square metres

Shoulder condition No damage, Moderate damage, Severe damage

Shoulder level Level or Low, High

Carriageway Shape Very Good, Good, Average, Poor, Very Poor, Failed

Effective width Length where width has receded by greater than 1 m

Crown height As built > 300 mm, 150-300 mm, < 300 mm

Surface condition Very Good, Good, Average, Poor, Very Poor

Ruts None, < 15 mm, 15 – 30 mm, > 50 mm

Corrugations None, < 15 mm, 15 – 30 mm, > 50 mm

Potholes None, 1-5, 5-10, >10 per 20 m sub-section

Loose material None, < 15 mm, 15 – 30 mm, > 50 mm

Oversize materials None, Yes (if 5% of the material > 50 mm)

5.1 Gravel loss

5.1.1 Country-specific results

The height of the road at each cross-section was estimated by taking theaverage of the readings over the

carriageway width at the cross-section. Theaverage height of the site was thendetermined by taking the average of the 11cross-sectional heights. The rates of gravelloss on each site were then determined bycomparing the average height of the sitefrom each survey. From the various ratesdetermined for each site, a typical rate ofgravel loss was selected for each site. Thesetypical rates of gravel loss were usually themaximum rates recorded between twosuccessive surveys on a site in an attempt toexclude the effects of any maintenance that

may have been carried out. The observedtypical rates of gravel loss for all the sitesare listed in Table 5.1.

The observed average rate of gravel loss waslowest in Zimbabwe with a rate of10 mm/year. The average rate observed inUganda was 16 mm/year, with the highestrate of 24 mm/year observed in Ghana.However, it should be noted that gravel loss isa function of material and traffic factors, andthe impact of these factors on the abovegravel loss figures is different in each country.

A more detailed examination of the gravelloss rates was conducted to determine theinfluence of variables such as traffic andmaterial properties.

The sites were divided into two groups;those with a wearing course that had a low

19

5. Performance of the Labour-Based ConstructedRoads

Figure 5.1Rates of gravel loss in Ghana

20


Table 5.1: Observed rates of gravel loss

Zimbabwe Ghana Uganda

Site Gravel Loss Site Gravel Loss Site Gravel Loss(mm/yr) (mm/yr) (mm/yr)

CUMA 1 9.7 AIAA 33.4 KAPA 25.4

CUMA 2 18.3 ANAO 14.7 KOMA 12.9

KAME 1 11.3 AOAA 11.8 MAMO 15.5

KAME 2 4.7 AODU 21.3 MOKA 15.7

KAME 3 3.2 AOKM 8.6 MOKO 21.3

MAMI 1 11.1 BACU 35.0 NEKI 12.6

MAMI 2 12.7 BEPO 28.6 NIBU 14.6

MEME 1 13.9 BMBE 8.7 RAKE 10.0

MEME 2 5.6 BNBN 62.8 AAvveerraaggee 1166..00

MEME 3 8.6 BNNA 22.7

MONE 1 19.9 DABI 14.9

MONE 2 13.2 DACO 36.9

MSMA 1 13.2 DEWN 12.0

MSMA 2 10.1 DOKO 52.4

NACO 1 9.8 FIDA 34.6

NACO 2 7.0 GNKI 11.8

NARA 1 7.8 LONM 15.3

NARA 2 10.6 NALU 27.0

NUKO 1 16.8 NUSG 30.7

NUKO 2 15.5 PEYI 13.8

PESE 1 13.1 SAKE 8.5

PESE 2 3.6 WAKA 22.5

SECO 1 9.7 WEPA 15.0

SECO 2 11.0 ZATE 22.6

SUMA 1 12.0 AAvveerraaggee 2233..66

SUMA 2 12.9

TEPU 1 12.2

TEPU 2 10.0

TEPU 3 5.8

TOSA 1 7.9

TOSA 2 3.7

AAvveerraaggee 1100..55

plasticity (Plasticity Product, PP < 300) andthose with a high plasticity (PP > 300). Therates of gravel loss for these two groups ofsites in Ghana have been plotted againsttheir traffic levels (ADT) in Figure 5.1.

Similarly for the Uganda sites, the rates ofgravel loss have been plotted against ADT

for sites with low and high plasticity wearingcourses as shown in Figure 5.2.

However, for the Zimbabwe sites, no distinctseparate trends could be identified betweenthe low and high plasticity sites as shown inFigure 5.3.

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Regional Report

Figure 5.2Rates of gravel loss in Uganda

Figure 5.3Rates of gravel loss in Zimbabwe

The rates of gravel loss estimated from thetrends shown in the above graphs havebeen given in Table 5.2. These valuesillustrate the high rates of gravel lossobserved on the low plasticity sitescompared with the high plasticity sites inboth Ghana and Uganda. The rates ofgravel loss on the high plasticity sites inthese two countries are similar to theaverage rates observed for all the sites inZimbabwe.

The figures in Table 5.2 indicate that forgravel roads with a 100 mm thick wearingcourse, regravelling is required every 3years for the low plasticity sites carryingapproximately 100 vehicles per day (vpd) inGhana and Uganda, whereas regravellingis required every eight years on the highplasticity sites and in Zimbabwe with similartraffic levels.

5.1.2 Combined results

For the next stage of the analysis, the datafrom all three studies were combined.

In order to assess the influence of thematerial properties in more detail, trafficwas standardised. Traffic was standardisedto 100 vpd and the observed gravel losswas adjusted to correspond to anequivalent gravel loss at an ADT of 100(GL100).

The plots in Figure 5.1, Figure 5.2 andFigure 5.3 illustrate that gravel lossincreases linearly with ADT. However, prior

to this adjustment, the gravel loss due toenvironmental factors (GLE) such as rainfalland wind needs to be taken into account.The minimum observed gravel loss in eachcountry was used as an estimate of GLE forthat country. The rate of gravel loss at100 vpd for each site was then derivedusing the formula:

GL100 = (GL – GLE)(100/ADT) + GLE

. . . (1)

GL100 was derived for all the sites and thesevalues of GL100 were used to rank theperformance of the sites. The performancesof the sites were then ranked as ‘Good’,‘Moderate’ or ‘Poor’ according to their‘standardised’ rates of gravel loss (GL100)using the thresholds given in Table 5.3.

Table 5.3: Gravel loss performancecriteria

Performance GL100(mm/year/100 vpd)

Good ≤ 25

Moderate 25 – 50

Poor > 50

The observed gravel loss on the sites in thethree countries have been plotted in Figure5.4, with each site identified by its gravelloss performance category and withseparate trend-lines fitted for eachcategory.

22


Table 5.2: Gravel loss trends

Gravel Loss (mm/year)

ADT Ghana Uganda Zimbabwe

PP < 300 PP > 300 PP < 300 PP > 300 All

20 21 10 17 10 10

50 26 11 21 11 11

100 35 12 28 12 13

150 43 13 34 13 15

200 52 14 41 14 17

250 61 15 48 15 19

The plot in Figure 5.4 illustrates that thetrend-lines converge to a gravel loss ofapproximately 7mm ± 1mm. This indicatesthat the environmental contribution togravel loss is significant at 7 mm/year andis consistent for the three performancecategories.

Detailed descriptions of the performances ofthe individual sites were documented in theappropriate country reports. These assess-ments indicated that material properties,primarily grading and plasticity, wereimportant factors in the performance of thesites, with the rates of gravel loss generallylower on sites that had a gravel wearingcourse that was fine with high plasticity.

The quality of the gravel wearing course canbe assigned to one of four ‘material qualityzones’, as illustrated in Figure 5.5. Thehigher quality materials (fine material withhigh plasticity) are represented by Zone A,where PP > 280 and GM < 1.9. Sites withthis high material quality would be expectedto perform well. The lower quality materialis represented by Zone D, where PP < 280and GM > 1.9. Sites with this low qualitymaterial would be expected to perform

poorly, with Zones B and C representingmaterial of marginal quality.

The plasticity product (PP) has been plottedagainst the grading modulus (GM) for eachsite in Figure 5.5. In this plot the sites havebeen identified by their gravel lossperformance category as listed in Table 5.3.Zone A (i.e. high quality material) containedsites whose performances have beencategorised as either Good or Moderate.The majority of the Poor performing siteswere in Zone D (i.e. low quality material),whereas Zones B and C (i.e. marginalquality material) having similar proportionsof all three performance category sites.

These findings support the hypothesis thatsites in Zone A are likely to perform well,marginally in Zones B and C, and poorly inZone D.

For the next stage of the analysis process,the representative rate of gravel loss foreach material quality zone was determined.The ‘standardised’ rates of gravel loss (i.e.GL100) of all the sites in each Zone wereplotted in a cumulative frequency graph, asshown in Figure 5.6.

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Regional Report

Figure 5.4Observed gravel loss by performance category

24


Figure 5.5Gravel loss performance categories vs material quality zones

Figure 5.6Cumulative frequency of standardised rates of gravel loss

The 50 percentile values were taken as therepresentative rates of GL100 for each Zone.These values are listed in Table 5.4.

Table 5.4: Representative rates ofgravel loss

Material Quality Representative Rate of Gravel LossZone GL100 (mm/yr/100 vpd)

A 12

B 20

C 29

D 32

These values indicate that sites with a gravelwearing course with material properties inZone A are likely to lose 12 mm/yr whenthe ADT is 100, whereas for Zone Dmaterial, the rate of gravel loss is a factor of3 times higher for similar traffic levels. Thereason behind this could be that bonding ismore enhanced in fine materials throughthe increased surface contact in situationswhere plastic materials exist in the matrix.Coarser materials, however, tend to haveless contact area and where the coarseaggregate is surrounded by fines the largerparticles get knocked off as a result ofwheel impact – this is usually evident asthese larger particles accumulate on roadshoulders into windrows.

The results also indicate that the grading ofthe material has a stronger influence ongravel loss than the plasticity of thematerial. The rates of gravel loss for siteswith wearing course material that wascoarse (i.e. Zones C & D) were on averagesignificantly higher than for sites with finematerial (Zones A & B), with plasticityhaving a less significant effect.

5.2 Roughness

5.2.1 Country-specific results

The roughness trends observed on the testsites have been plotted and described indetail in the relevant country reports. Theaverage roughness over the monitoring

period observed on each site has beenlisted in Table 5.5. These values indicatethat the average roughness of the sites inGhana and in Uganda were similar at 6 IRI,whereas the sites in Zimbabwe were, onaverage, in a poorer condition with anaverage roughness of approximately 10 IRI.

The rates of roughness progressionbetween surveys on each site wereexamined. Roughness on unsealed roadscan be variable over short periods of time.Decreases in roughness are caused byroutine maintenance activities such as spotregravelling or grading. Also, vehicles tendto travel on the least rough areas of thecarriageway. Therefore when roughness ismeasured in the wheelpaths, as in thisproject, then decreases in roughness maybe recorded simply due to vehiclestravelling in different wheelpaths.

For these reasons, positive rates ofroughness progression were not recordedon a few sites. For the other sites, theobserved rates of roughness progressionshave been listed in Table 5.6.

The average rates of roughness progressionobserved in Ghana and in Uganda weresimilar at approximately 3 IRI/year, whereasin Zimbabwe the average rate was lower at2 IRI/year, despite the average roughnessvalues in Zimbabwe being higher than inthe other two countries (see Table 5.5).

5.2.2 Combined results

As for gravel loss, the data from all threestudies were combined for the next stage ofthe roughness analysis.

The rates of roughness progression werestandardised to a traffic level of 100 vpd.However, because of the fluctuations inroughness on unsealed roads (as discussedearlier), no environmental component couldbe determined. Thus the rate of roughnessprogression per 100 vpd (IRI/year/100 vpd –IRI100) was determined as a simple ratio ofthe observed rate of progression (IRI/year -IRIobs) as follows:

IRI100 = IRIobs(100/ADT) . . . (2)

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26


Table 5.5: Observed average roughness


Site Average Roughness Site Average Roughness Site Average Roughness(IRI) (IRI) (IRI)


CUMA 2 12.7 ANAO 4.2 KOMA 8.4

KAME 1 9.3 AOAA 5.7 MAMO 10.3

KAME 2 8.5 AODU 5.8 MOKA 3.7


MAMI 1 7.7 BACU 4.7 NEKI 4.9

MAMI 2 8.8 BEPO 5.5 NIBU 6.3



MEME 3 10.9 BNNA 7.4

MONE 1 9.8 DABI 4.1

MONE 2 7.7 DACO 7.4

MSMA 1 9.3 DEWN 8.1

MSMA 2 7.2 DOKO 8.1

NACO 1 7.7 FIDA 4.2

NACO 2 8.2 GNKI 4.1

NARA 1 8.0 LONM 9.3

NARA 2 11.6 NALU 6.6

NUKO 1 13.1 NUSG 4.4

NUKO 2 10.8 PEYI 3.7

PESE 1 10.7 SAKE 4.3

PESE 2 8.5 WAKA 5.8

SECO 1 9.9 WEPA 7.5

SECO 2 11.2 ZATE 4.3


SUMA 2 9.7

TEPU 1 8.7

TEPU 2 12.0

TEPU 3 10.7

TOSA 1 7.9

TOSA 2 8.8


27

Regional Report

Table 5.6: Observed rates of roughness progression


Roughness Roughness RoughnessSite Progression Site Progression Site Progression

(IRI/yr) (IRI/yr) (IRI/yr)


CUMA 2 2.6 ANAO 0.4 KOMA 2.1

KAME 1 0.8 AOAA 2.2 MAMO 3.9

KAME 2 n/a AODU 3.7 MOKA 0.6


MAMI 1 n/a BACU 2.7 NEKI 2.7

MAMI 2 0.5 BEPO 2.2 NIBU 2.7



MEME 3 0.1 BNNA 1.8

MONE 1 1.8 DABI 1.4

MONE 2 2.2 DACO 0.1

MSMA 1 n/a DEWN 1.6

MSMA 2 n/a DOKO 4.5

NACO 1 2.2 FIDA 7.2

NACO 2 2.9 GNKI 1.2

NARA 1 8.1 LONM 5.4

NARA 2 1.6 NALU n/a

NUKO 1 1.8 NUSG 5.0

NUKO 2 3.4 PEYI 3.4

PESE 1 2.9 SAKE 1.8

PESE 2 0.6 WAKA 5.4

SECO 1 2.7 WEPA 6.3

SECO 2 0.3 ZATE 4.0


SUMA 2 1.1

TEPU 1 1.7

TEPU 2 0.7

TEPU 3 n/a

TOSA 1 0.6

TOSA 2 5.5


Notes n/a – positive rates of roughness progression not recorded on these sites

The performances of the sites were thenranked as ‘Good’, ‘Moderate’ or ‘Poor’according to their ‘standardised’ rates ofroughness progression (IRI100) using thethresholds given in Table 5.7.

Table 5.7: Roughness performanceCriteria

Performance IRI100(IRI/year/100 vpd)

Good ≤ 3

Moderate 3 – 6

Poor > 6

The observed rates of roughnessprogression on the sites in the threecountries have been plotted in Figure 5.7,with each site identified by its roughnessperformance category and with separatetrend-lines fitted for each category.

The trend-lines for the three roughnessperformance categories illustrated in Figure5.7 converge to a roughness of < 1 IRI/year.As this level of roughness progression can beregarded as relatively insignificant forunsealed roads, the environmentalcontribution to roughness progression wasassumed to be negligible.

Using the same four material quality zonesas for gravel loss, the sites have beenidentified by their roughness performancecategory as illustrated in Figure 5.8. As forgravel loss, Zone A had proportionally moreof the better performing sites, whereasZone D contained the majority of the Poorperforming sites.

The representative rate of roughnessprogression for each material quality zonewas determined by plotting the ‘stan-dardised’ rates of roughness progression(i.e. IRI100) of all the sites within each Zonein a cumulative frequency graph, as shownin Figure 5.9.

28


Figure 5.7Observed rates of roughness progression by performance category

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Regional Report

Figure 5.8Roughness performance categories vs material quality zones

Figure 5.9Cumulative frequency of standardised rates of roughness progression

The 50 percentile values were taken as therepresentative rates of IRI100 for each Zone.These values are listed in Table 5.8.

Table 5.8: Representative rates ofroughness progression

Material Quality Representative Rate of Roughness Zone Progression IRI100 (IRI/yr/100 vpd)

A 2.0

B 2.3

C 4.4

D 9.5

These values indicate that sites with a gravelwearing course with material properties inZone A are likely to increase in roughnessby 2 IRI/year when the ADT is 100, whereasfor Zone D material, the rate of roughnessprogression is almost 5 times faster forsimilar traffic levels.

30


One of the objectives of this projectwas to compare the observed ratesof deterioration on the test sites

with those predicted by HDM-4. Forunsealed roads, HDM-4 predicts the rate ofgravel loss and the rate of roughnessprogression. A comparison between thesepredicted rates and those observed on thetest sites is described below.

6.1 Gravel loss

Regravelling is the major maintenanceoperation on unsealed roads, analogous inimportance to the overlaying of a pavedroad, so the frequency required is animportant planning decision. Gravel loss isdefined as the change in gravel thicknessover a period of time and is used toestimate when the thickness of the gravelwearing course has decreased to a levelwhere regravelling is necessary.

The HDM-4 relationship for predicting theannual quantity of gravel loss is a functionof rainfall, traffic volume, road geometryand characteristics of the gravel and isgiven below.

GL = Kgl 3.65 [3.46 + 0.246(MMP/1000)(RF) + (KT)(AADT)]

where

KT = Kkt max [0, 0.022 + 0.969(HC/57300) +0.00342(MMP/1000)(P075) - 0.0092(MMP/1000)(PI) – 0.101(MMP/1000)]

and

GL = annual material loss, in mm/year

KT = traffic-induced material whip-off coefficient

AADT = annual average daily traffic, in vehicles per day

MMP = mean monthly precipitation, in mm/month

RF = average rise plus fall of the road, in m/km

HC = average horizontal curvature of the road, in deg/km

P075 = amount of material passing the 0.075 mm sieve, in % by mass

PI = plasticity index of the material, in %

Kgl = calibration factor for material loss

Kkt = calibration factor for traffic-induced material whip-off coefficient

The rates of material loss predicted by theabove relationship have been illustrated inFigure 6.1 for a range of traffic levels andrainfall for an unsealed road in flat terrain.

The HDM-4 predicted rates of gravel loss forthe sites were compared with the rates ofgravel loss observed on the sites. The HDM-4 model was then calibrated so that thepredicted rate matched the observed rate oneach site. As discussed in Section 5.1, for thesites in Ghana and Uganda, two distinctrates of gravel loss were observed, one forsites with low plasticity and one for sites withhigh plasticity. In Zimbabwe no distinctioncould be determined between sites with lowor high plasticity. The results of thiscalibration for the individual sites in eachcountry are detailed in the relevant countryreports and summarised in Table 6.1.

The default value of the calibration factorfor gravel loss, Kgl, in HDM-4 is 1.0. Thevalues of Kgl in Table 6.1 indicate that onaverage, the gravel loss observed on thesites in Ghana was approximately 50%higher than predicted by HDM-4 for theconditions (rainfall, traffic, material

31

6. Comparison with HDM-4 Models

properties) encountered in Ghana.Similarly, the gravel loss observed on thesites in Uganda were approximately 20%higher than predicted by HDM-4, whereasin Zimbabwe the observed rates were onaverage some 35% below the HDM-4predicted values.

In both Ghana and Uganda the values ofKgl were significantly higher for the lowplasticity sites than for the high plasticitysites, indicating that the observed rates ofgravel loss in these countries were moresensitive to plasticity than indicated by theHDM-4 model.

As described in Section 5, the sites weregrouped into four material quality zones

and the performance of each group of siteswas examined. The average value of Kgl forall the sites in each zone was derived andthese ‘representative’ gravel loss calibrationfactors have been listed in Table 6.2.

Table 6.2: Representative gravel losscalibration factors

Material Quality Representative Gravel LossZone Calibration Factor Kgl

A 0.78

B 0.90

C 1.22

D 1.31

The values of Kgl in Table 6.2 indicate thatHDM-4 predicts higher rates of gravel lossthan were observed on sites with finewearing course material (Zones A & B),whereas for the sites with coarse material(Zones C & D), HDM-4 predicted lower ratesof gravel loss than were observed.

6.2 Roughness

In HDM-4, the roughness progressionrelationship constrains the roughness to ahigh upper limit, or maximum roughness(RImax), by a convex function in which the

32


Table 6.1: Average country-specificgravel loss calibration factors

Country Sites Calibration FactorKgl

Ghana Low plasticity 2.43High plasticity 0.86All 1.51

Uganda Low plasticity 1.41High plasticity 0.80All 1.18

Zimbabwe All 0.65

Figure 6.1HDM-4 predicted rates of gravel loss

rate of progression decreases linearly withroughness to zero at RImax as illustrated inFigure 6.2.

The maximum roughness is a function ofmaterial properties and road geometry. Therate of roughness progression is a functionof the existing roughness, maximum rough-ness, time, traffic and material properties.The roughness progression relationship isgiven by:

RITG2 = RImax – b [RImax – RITG1]

where

RImax = max{[21.5 – 32.4(0.5 - MGD)2 + 0.017(HC) – 0.764(RF)(MMP/1000)],11.5}

b = exp [c(TG2 – TG1)] where 0 < b < 1

c = - 0.001 Kc [0.461 + 0.0174 (ADL) + 0.0114(ADH) - 0.0287(ADT)(MMP/1000)]

and

RITG1 = roughness at time TG1, in m/km IRI

RITG2 = roughness at time TG2, in m/km IRI

RImax = maximum allowable roughness for specified material, in m/km IRI

TG1, TG2 = time elapsed since latest grading, in days

ADL = average daily light traffic (GVW < 3500kg) in both directions, in vehicles per day

ADH = average daily heavy traffic (GVW ≥ 3500kg) in both directions, in vehicles per day

ADT = average daily vehicular traffic in both directions, in vehicles per day

MMP = mean monthly precipitation, in mm/month

HC = average horizontal curvature of the road, in deg/km

RF = average rise plus fall of the road, in m/km

MGD = material gradation dust ratio

= P075 / P425 if P425 > 0

= 1 if P425 = 0



Kc = calibration factor for roughness progression

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Regional Report

Figure 6.2Roughness progressions on unsealed roads with no maintenance

The roughness progression relationshipgiven above was derived using observationsfrom roads under repeated grading cycleswith no special compaction. The rates ofroughness progression after construction orrehabilitation with full mechanical shapingand compaction were observed to be muchslower than given by this model.

Thus if "mechanical compaction" is used, thecoefficient c is reduced, initially to onequarter of its predicted value and rising tothe full predicted value after a few gradingcycles, but in a period not exceeding 4years, as follows:

c’ = c {min [1, 0.25(t) max (1, n0.33)]}

where

t = time since regravelling or construction with mechanical compaction, in years

n = frequency of grading, in cycles/year

and

b’ = exp[365(c’/n)]

When mechanical compaction is specified,then b’ and c’ are used in place of b and crespectively in the roughness progressionrelationship.

Maintenance, in the form of grading, onunsealed roads tends to reduce the level ofroughness. The HDM-4 relationship forpredicting this reduction in roughness is afunction of the roughness before grading,the material properties and the minimumroughness (RImin). The minimum roughness,below which grading cannot reduceroughness, increases as the maximumparticle size increases and the gradation ofthe surfacing material worsens.

The HDM-4 relationship for predicting theroughness after grading is expressed as alinear function of the roughness beforegrading, dust ratio and the minimumroughness, as follows:

RIag = RImin + a [RIbg - RImin]

where

a = Ka max{0.5, min [GRAD [0.553 + 0.23(MGD)], 1]}

RImin = max {0.8, min [7.7, 0.36(D95)(1 - 2.78MG)]}

and

RIag = roughness after grading, in m/km IRI

RIbg = roughness before grading, in m/km IRI

RImin = minimum allowable roughness after grading, in m/km IRI

D95 = maximum particle size of the material, defined as the equivalentsieve size through which 95% of the material passes, in mm

MG = slope of mean material gradation

MGD = material gradation dust ratio


GRAD = 1.4 for non-motorised grading, bush or tyre dragging

= 1.0 for light motorised grading, little or no water and no roller compaction

= 0.7 for heavy motorised grading, with water and light roller compaction

Ka = calibration factor for the effect of grading

The slope of mean material gradation iscalculated as follows:

MG = min [MGM, (1 - MGM), 0.36]

where

MGM = (MG075 + MG425) + MG02) / 3

MG075 = loge(P075/95) / loge(0.075/D95)

MG425 = loge(P425/95) / loge(0.425/D95)

MG02 = loge(P02/95) / loge(2.0/D95)

The HDM-4 predicted rates of roughness forthe sites were compared with the roughnessobserved on the sites. It was assumed thatlight motorised grading with little or nowater and no roller compaction was usedon an annual basis (i.e. GRAD = 1.0). TheHDM-4 roughness model was thencalibrated so that the predicted roughnessmatched the average roughness observedon the site during the two-year monitoring

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period. The results of this calibration for theindividual sites in each country are detailedin the relevant country reports.

The default value of the calibration factorfor roughness progression, Kc, in HDM-4 is1.0. The average value of Kc derived foreach country was 0.6, 0.8 and 0.9 forGhana, Uganda and Zimbabwerespectively. This indicates that the rates ofroughness progression observed on thesites were on average lower than thosepredicted by HDM-4.

As described in Section 5, the sites weregrouped into four material quality zonesand the performance of each group of siteswas examined. The average value of Kc forall the sites in each zone was derived andthese ‘representative’ roughness pro-gression calibration factors are listed inTable 6.3.

Table 6.3: Representative roughnessprogression calibration factors

Material Quality Representative Roughness Zone Progression Calibration Factor Kc

A 0.34

B 0.65

C 1.08

D 1.31

The values of Kc in Table 6.3 indicate thatHDM-4 predicts higher rates of roughnessprogression than were observed on siteswith fine wearing course material (Zones A& B), whereas for the sites with coarsematerial (Zones C & D), HDM-4 predictedlower rates of roughness progression thanwere observed.

The average material properties of all thesites in each Zone were used as therepresentative material properties for thatZone in estimating the HDM-4 predictedeffect of grading on roughness. Therepresentative material properties for eachZone that are required by HDM-4 are listedin Table 6.4.

These values were used to estimate theroughness after grading (RIag) using theHDM-4 relationship. As for roughnessprogression, it was assumed that lightmotorised grading with little or no waterand no roller compaction was used (i.e.GRAD = 1.0). The HDM-4 predicted effectof grading for a range of roughness levelshave been tabulated in Table 6.5.

These decreases in roughness due tograding have been used in the life-cyclemethodology.

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Regional Report

Table 6.4: Representative material properties

Maximum Percentage Material Percentage Material Percentage MaterialZone Particle Size Passing 2.0 mm Sieve Passing 0.425 mm Sieve Passing 0.075 mm Sieve

(D95) (P02) (P425) (P075)

A 9.5 61.5 45.0 29.0

B 7.1 73.5 40.8 19.5

C 19.0 47.0 35.0 34.0

D 19.0 47.3 24.8 14.0

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Table 6.5: Effect of grading on roughness

Zone Roughness Before Grading Roughness After Grading Decrease in Roughness Due(RIbg) (RIag) to Grading(dIRI)

A 6.0 4.5 1.57.0 5.2 1.88.0 5.9 2.19.0 6.6 2.4

10.0 7.3 2.7

B 6.0 4.2 1.87.0 4.9 2.18.0 5.6 2.49.0 6.2 2.8

10.0 6.9 3.1

C 6.0 5.1 0.97.0 5.9 1.18.0 6.7 1.39.0 7.4 1.6

10.0 8.2 1.8

D 6.0 4.4 1.67.0 5.0 2.08.0 5.7 2.39.0 6.4 2.6

10.0 7.1 2.9

The quality of the gravel wearing courseon each site has been assigned to oneof four ‘material quality zones’ as

illustrated in Figure 7.1. The higher qualitymaterials are represented by Zone A, wherePP > 280 and GM < 1.9. Sites with thismaterial quality would be expected toperform well. The low quality material isrepresented by Zone D, where PP < 280and GM > 1.9. Sites with this qualitymaterial would be expected to performpoorly, with Zones B and C representingmaterial of marginal quality.

The sites from the studies in the threecountries (i.e. Ghana, Uganda andZimbabwe) have been assigned to one ofthe four ‘material quality zones’ based on

the properties of their gravel wearingcourse. The performances of the sites wereassessed in terms of rates of gravel loss androughness progression, and representativerates for each Zone were derived (see Table5.4 for gravel loss and Table 5.8 forroughness progression).

The rates of gravel loss given in Table 5.4are representative of the rates in each Zonefor a traffic level of 100 vpd. These ratescan be transformed into rates for differentlevels of traffic using equation (1) shown inSection 5.1. From these rates, the frequencyof regravelling for material in each of thefour Zones can be estimated for a range oftraffic levels. An example of regravelling toa thickness of 150 mm when the gravel

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7. Life-Cycle Cost Methodology

Figure 7.1Material quality zones

thickness has been reduced to 25 mm hasbeen plotted in Figure 7.2 for traffic levelsup to 500 vpd.

This figure shows that for a traffic level of10 vpd, the regravelling frequency rangesfrom approximately every 17 years formaterial in Zone A to every 13 years formaterial in Zone D. For higher traffic levelsof 100 vpd, the regravelling frequencyincreases to every 10 years for Zone A andevery 4 years for Zone D.

Using graphs such as that illustrated inFigure 7.2, the number of times a road willneed to be regravelled over its life can beestimated when the quality of the gravelwearing course and the traffic volume isknown. The cost of regravelling over the lifeof the road can then be estimated.

In addition to regravelling costs, life-cyclecosts also include initial construction orrehabilitation costs, grading and otherroutine maintenance costs such as spotregravelling, vegetation control, etc. Thefrequency of some these routine

maintenance activities will depend onperceived acceptable conditions of roads forvarious levels of traffic. Average figures forroutine maintenance can be obtained fromhistorical data or Bill of Quantities or unitrates used by organisations.

The rates of roughness progression given inTable 5.8 are representative of the rates ineach Zone for a traffic level of 100 vpd.These rates can be transformed into ratesfor different levels of traffic using equation(2) shown in Section 5.2.

The effect of grading on roughness hasbeen estimated from HDM-4 for each Zone,as tabulated in Table 6.5. These figures givethe reduction in roughness for differentlevels of roughness intervention levels.From these reductions in roughness and therepresentative rates of roughnessprogression, the number of gradings over aperiod of time can be estimated.

An example of the number of gradingsrequired over a 20-year period using anintervention level of 6 IRI to trigger grading

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Figure 7.2Regravelling frequency example

has been plotted in Figure 7.3 for trafficlevels up to 500 vpd.

This figure shows that for a traffic level of100 vpd, the number of gradings over 20years will be approximately 20 (i.e. once ayear) for material in Zone A, increasing toapproximately 120 gradings for material inZone D (i.e. 6 gradings per year), if agrading is triggered whenever theroughness reaches 6 IRI. This is based onprinciples of performance-based main-tenance where desirable or acceptablestandards are set beyond which remedialmeasures should be taken. Performance-based maintenance is becoming morewidely used, especially where the use of theprivate sector is increasingly taking overfrom force account. The difference in thenumber of grading for Zone A and Zone Dclearly shows how the choice of materialsduring construction, rehabilitation orregravelling will impact on the futuremaintenance of the road.

From graphs such as that illustrated inFigure 7.3, the number of times a road willneed to be graded over its life can beestimated, knowing the quality of the gravelwearing course and the traffic volume. Thecost of grading over the life of the road canthen be estimated.

A spreadsheet-based program has beendeveloped for computing life-cycle costs forvarious levels of traffic and for the differentmaterial quality zones as illustrated in thefictitious example in Figure 7.3.

The illustrated example was developedusing fictitious unit costs for the con-struction, regravelling, grading and otherroutine maintenance activities. The totalcosts of regravelling and grading wereestimated based on the frequency of eachactivity using graphs such as thoseillustrated in Figure 7.2 and Figure 7.3.

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Regional Report

Figure 7.3Grading frequency example

The life-cycle cost calculator requires thefollowing inputs:

� Average Daily Traffic (ADT)� Currency� Gravel thickness after regravelling� Minimum gravel thickness for triggering

regravelling� Cost of regravelling� Cost of grading� Cost of other routine maintenance

� Cost of original construction/rehabil-itation (optional)

� Life-cycle analysis period� Roughness after regravelling� Roughness for triggering grading.

These unit costs, as well as the otherparameters will need to be adjusted in thespreadsheet program with country-specificdata.

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Figure 7.4Life-cycle cost example

The implementation of the outputs andresults from this project could have asignificant impact on the manner in

which labour-based technology is appliedand low-volume unpaved roads aremanaged. The conclusions below put theobjectives of the project and the fulfilmentof them into perspective.

Deterioration relationships

The deterioration of gravel roads in terms ofrates of gravel loss and roughnessprogression have been investigated andquantified. Material properties and trafficlevels have been shown to be highlysignificant parameters affecting gravel lossand roughness progression.

The plots in Figure 5.4 indicate that at zerotraffic the environmental-induced rate ofgravel loss is similar for all wearing coursematerials regardless of material properties.This environmental contribution to gravelloss is 7 mm/year; i.e. 7 mm of gravel is losteach year due to environmental factors,even on roads with zero traffic levels.

The plots in Figure 5.7 indicate that theenvironmental contribution to roughnessprogression was negligible.

The quality of the gravel wearing coursewas assigned to one of four ‘materialquality zones’:

� Zone A (High Quality)– PP> 280 and GM < 1.9

� Zone B (High / Marginal Quality)– PP < 280 and GM < 1.9

� Zone C (Marginal / Poor Quality)– PP > 280 and GM > 1.9

� Zone D (Low Quality)– PP < 280 and GM > 1.9

Representative rates of gravel loss werederived for each material quality zone as12, 20, 29 and 32 mm per year per 100 vpdfor Zones A, B, C and D respectively.

Similarly, representative rates of roughnessprogression were derived for each materialquality zone as 2.0, 2.3, 4.4 and 9.5 IRI peryear per 100 vpd for Zones A, B, C and Drespectively.

The HDM-4 predictive relationships forunsealed roads were compared with theobserved rates of gravel loss and roughnessprogressions. From the comparison,representative calibration factors for theHDM-4 models were derived for eachmaterial quality zone.

The representative calibration factors forgravel loss were derived as 0.78, 0.90, 1.22and 1.33 for Zones A, B, C and Drespectively. Similarly, representative cali-bration factors for roughness progressionwere derived as 0.34, 0.65, 1.08 and 1.31for Zones A, B, C and D respectively.

These results mean that HDM-4 can beused with some confidence for predictingrates of deterioration of gravel roadsconstructed by labour-based methods byusing these calibration factors.

Life-Cycle costs

A life-cycle costing methodology has beendeveloped using the results of the observedrates of deterioration. The life-cycle costtool calculates the agency costs over aperiod of time. Though there are options tovary the length of the life-cycle of a road,the default suggested is a 20-year period.This is deliberately chosen so that the costscan then be compared with those of sealed

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8. Conclusions

roads of similar standards whose design lifeis commonly accepted to be 20 years.

For each material quality zone, the toolcalculates the regravelling frequency basedon the representative gravel loss ratesderived in this project. User-specifiedroughness intervention criteria are used totrigger grading activities. The HDM-4relationship for estimating roughness aftergrading is used to model the gradingeffects. The total costs of regravelling andgrading over the life-cycle period are thencalculated. The costs of other routine main-tenance (e.g. spot regravelling, vegetationcontrol, etc.) and original construction costscan also be added.

The tool calculates the total life-cycleagency costs discounted over the analysisperiod.

Overall project goal and purpose

The goal of this project was ‘To promotesustainable livelihoods and contribute to thesocio-economic development of dis-advantaged rural populations through theprovision of improved road access’ and thepurpose was ‘To reduce the life-time costs ofunpaved rural roads by promotingappropriate engineering standards, planningtools and works procedures for labour-basedconstruction and maintenance’.

The development of:

i) deterioration relationships for gravelloss and roughness progression basedon the quality of the wearing coursematerial

ii) performance related wearing coursespecifications

iii) a life-cycle costing tool as an aid forplanning and designing unpaved roads

iv) the Guideline with quality assuranceprocedures and specifications includingapproval of materials and works, andmethod specifications type of qualitycontrol

All contribute towards the fulfilment of theproject goal and objective.

The outputs from this project provide roadauthorities with information and tools foruse in the design of rural unpaved roadsthat are likely to perform well and alsoassist in estimating the agency costsassociated with their sustenance. The use ofthese outputs gives the hope of improvedroad access for rural and peri-urbancommunities, contributing to thesustainable livelihoods and socio-economicdevelopment of the disadvantagedpopulations and the increased applicationof local resources.

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The results of this research show thatthe whole perspective of rural andperi-urban road provision has to

change. There are many aspects associatedwith the output stated above that can makesignificant differences in the provision ofrural roads in the short-, medium- andlong-term.

First and foremost, the research findingsneed to be implemented in order for thebenefits to be realised:

1. Policy change: The policies governingthe provision of low-volume unpavedroads need to be reviewed. Without thispolicy review it will be difficult forpractitioners to implement on a widescale. If this does not happen then thebenefit will be relatively insignificant.

2. Training of practitioners: Practitionersshould be trained on the application ofthese findings and outputs throughshort courses in their field of work usingtheir specific projects and real data. Thismethod of training is more effectivethan formal classroom lectures.

3. Increased awareness: Obviously,training alone could never reach alldeserving practitioners and it may benecessary to carry out seminars,workshops, websites, etc. to try and getthe information across to as manyintended beneficiaries as possible.

4. The life-cycle costing tool should beassessed for applicability in a number ofcountries. If adopted, a user manual forthe life-cycle costing software will needto be produced in order to help thepractitioners.

5. Technology choice: In line with

increased commitments made atnational, regional and global levels onpromoting the use of employment-friendly approaches in the delivery andmaintenance of infrastructure works,efforts has to be made to translate thesecommitments into practice. The findingsof this research widen the scope for theapplication of employment-intensiveapproaches in the road sector.

While the life-cycle costing software providesa tool for estimating agency costs forunpaved roads, it would be advantageousfor practitioners to be able to compare theoutput of life-cycle costs for the gravel optionwith those of the low-cost sealed roads of thesimilar standard. This entails determininglife-cycle costs for low-cost sealed roads. Thiswill help in setting thresholds on when toupgrade to sealed road standards in order tominimise life-cycle costs.

There is a general bias towards machine-based construction methods because oftheir use of modern equipment, which givesthe perception of better quality productsthan its labour-based counterpart. One ofthe anticipated outcomes of this project wasthe increased application of labour-basedtechnology. Comparisons made in differentcountries, e.g. Lesotho, Mozambique, SouthAfrica, Tanzania etc. between roadsconstructed using labour-based andmachine-based methods have documentedthe competitiveness and benefit of labour-based methods. Labour-based methodshave proven competitive in terms oftechnical quality, with the financial andeconomical cost of construction becoming acatalyst for local economic development.

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9. Recommendations

The Quality Assurance Guideline may needto be streamlined in order to promote itsuse and thus improve the quality of labour-based works and roads. There is generalconsensus that there has been littleemphasis on quality assurance in the past.The Quality Assurance Guideline will allayclient scepticism as to value for money fortheir investment. Streamlining can beachieved through trialling, awareness,training and dissemination. Trialling is themost crucial part of the process and this canbe arranged relatively easily and quickly as

there are numerous on-going labour-basedworks. As this document benefits the clientswith good value for money, contractorsthrough speedy and objective approvalprocesses, consultants through simplerquality assurance procedures, designerswith design tools and specifications,decision-makers with life-cycle costinginformation and communities with betterand more sustainable roads, etc.stakeholder support for the streamliningprocess should be forthcoming.

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increased application of labour-based methods unpaved rural roads by promoting appropriate...

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