CHAPTER 2

REVIEW OF LITERATURE

2.1 GENERAL

India's road network, 3.3 million kilometers long is the second largest road

network in the world after the USA, carrying 85% of passenger and 70% of freight

traffic. Eighty one per cent of the total network constitutes Low-Volume Roads

(LVRs) comprising of Other District Roads (ODR) and Village Roads (VR), which

were till recently not designed and constructed with quality control and not maintained

properly. The rural roads provide primary links to the highway transportation

system. Earlier there was no scientific planning for developing rural road

network. Construction techniques become costly in the absence of proper usage

of locally available materials and equipment. The funds allotted for the upkeep

and timely maintenance of rural roads are limited. Consequently improper and

inadequate maintenance of rural roads leads to premature failure demanding high

maintenance costs at a later stage. Considering the importance of rural roads in

the development of economy of the country, Government of India had launched a

major rural roads programme of giving all-weather access to rural areas, known as

Pradhan Mantri Gram Sadak Yojana in the year 2000.

The major maintenance activities for LVRs constructed under various schemes

have been elaborated in Indian Roads Congress (2002) special publication. The broad

guidelines for type and periodicity of renewals recommended are based on the

subjective judgments and past experience of field engineers. These guidelines

primarily depend on the traffic in Commercial Vehicles Per Day (CVPD) and yearly

rainfall intensity (mm/year). Hence it is strongly felt that there is an immediate need

11

for suggesting a scientific optimal maintenance policy for preservation of this huge

network of LVRs in India.

As rural connectivity programme of Government of India is gathering

momentum, large kilometers of rural roads have been constructed in recent years under

PMGSY and by taking financial assistance from National Bank for Agricultural and

Rural Development (NABARD). Flexible pavements form a considerable part of the

rural road network in our country. Though construction cost of these roads is less

compared to that of concrete pavements, the rate of deterioration of flexible pavements

is much higher and hence maintenance cost involved is higher. Hence there is also an

urgent need for optimising the constrained budget provision for the maintenance of

LVRs, by incorporating the pavement deterioration models developed exclusively for

Low Volume Roads.

The rural roads generally suffer from vanous modes of distresses like

ravelling, potholes, edge failure and cracking. Various factors which accelerate

deterioration of roads include traffic loads, properties of soil, construction quality,

environmental factors etc. Lack of provision of proper drainage facilities has also been

realised as a major causative factor for deterioration of rural roads. Limited fund

allocation has been preventing the authorities from performing timely periodic

maintenance activities and consequently the rural road network in the country is

deteriorating at an alarming rate. Even though the function of pavement varies with the

specific user, the purpose of pavement is to serve traffic safely, comfortably and

efficiently at a minimum or at a reasonable cost. The Road User Cost Study (Final

Report, Road User Cost Study, 1982) in India has established that due to improper

maintenance and poor surface conditions of pavements, there is considerable economic

loss to the country due to increase in vehicle operation costs. If the pavements are

12

maintained to the desired level at appropriate time, it is possible to save the losses in

road user cost. But the budgetary pressures on authorities are forcing a reexamination

of economic priorities. Under these circumstances it is really important to resort to a

proper Pavement Maintenance and Management System studies to optimise the flow of

funds for maximising benefits. The World Bank in 1969 initiated the Highway Design

and Maintenance Standards study. Based on its extensive studies, the Highway Design

and Maintenance series models were developed, of which Highway Design and

Management Model, HDM-4 which is based on the concept of pavement life cycle cost

analysis is the latest version.

The failure of flexible pavements, which is a gradual process, occurs due to

internal damage caused by various factors such as traffic carried and environmental

factors. This damage gets accumulated with time and this process is termed as

deterioration. The pavement is said to be failed when this accumulated damage

exceeds the limiting state or serviceability level. The measure of distress of a pavement

is an indicator of the pavement performance.

The pavement distresses can be broadly classified as: i) Fracture

ii) Disintegration and iii) Deformation (IRe: 82-1982). The distresses are further

divided into different categories by distress type. The major types of distress that occur

in flexible pavements are i) load associated cracking (fatigue cracking) ii) thermal

cracking at low temperatures iii) longitudinal cracking at edges due to moisture

movement through shoulder iv) reflection cracking v) load associated permanent

deformation like transverse distortion or rutting and longitudinal distortion or

roughness vi) non-load associated pavement distortion due to foundation movements

vii) dis~ntegration (ravelling, stripping, pothole etc.).

13

Repeated traffic loads induce stresses and strains in pavement layers which

results in fatigue cracking, rutting and roughness. The properties of materials used in

pavement layers and quality of construction influence the rate of deterioration. A road

constructed with proper thickness design and quality control has a prolonged service

life. Weathering has an important role in the deterioration of flexible pavement.

Improper drainage causes ponding on the pavement which leads to development of

potholes. Moreover, moisture percolating into the pavement layers makes the subgrade

weak. The deterioration of pavement during its service life is a continuous process

which eventually makes Maintenance and Rehabilitation (M&R) action mandatory.

2.2 EVALUATION OF FLEXIBLE PAVEMENTS

Pavement performance is a term that quantifies the change of pavement

condition or the degree of service of their intended function with accumulating use

(Lytton, 1987; Michael, 1994). Pavement performance evaluation is the process of

assessing the structural and functional condition of an in-service pavement. The data

collected in pavement evaluation can be classified into three categories

i) Pavement history data

ii) Structural Condition data

iii) Functional Condition data

Pavement condition survey may be performed by walking along the pavement

section or by wind screen method (Michael, 1994; Haugodegard et aI., 1994).

The disadvantage of this method being highly time consuming is overcome by a

'photo logging system' in which a series of photographs of the pavement surface is

taken by the photo logging vehicle and stored in laser video discs for later use (Oh.,

1998). The use of a full digital computer based highway information system developed

by Wang et ai. (1998) is a still more advantageous method. Many agencies have

14

explored the advantages of GIS in the development of a PMS (Cheetham and Beck,

1994; Osman et aI., 1994). Kansas Department of Transportation uses GIS and other

spatial data management and analysis technologies for collecting, managing,

integrating, analyzing and presenting data (Fintsch et aI., 2004).

2.2.1 Pavement History Data

Pavement History data is a one-time data usually collected from the records of

road authorities. The details like, year of construction of the pavement, year of last

maintenance, type of maintenance carried out, traffic details for the past years etc. are

collected from the authorities. Pavement history data for the pavement helps in

judicious assessment of pavement performance.

2.2.2 Structural Condition Evaluation

Deterioration of a pavement is closely related to its structural adequacy. The

structural condition of pavement is described by parameters like

i) Thickness of various pavement component layers

ii) Relative Compaction of subgrade

iii) CBR of subgrade soil

iv) Deflection of pavement

Thickness of pavement layers is measured in the field by cutting a test pit

through the pavement layers. Field density of subgrade is determined by field tests like

sand replacement method. CBR values can be determined in the laboratory or can be

assessed from the Dynamic Cone Penetrometer (DCP) values with the help of

correlation charts. The combined effect of layer thickness and subgrade CBR is usually

represented as a single parameter called Modified Structural Number (MSN). MSN is

15

calculated using the equation 2.1 which was developed in Kenya Study, Hodges et al.

(1975).

MSN = SN + 3.51 (logIOCBR) -0.85 (logIOCBR) 2 - 1.43

where, SN is the Structural Number which is calculated using equation 2 .2.

SN = Iajti

(2.1)

(2.2)

where, ai is the strength coefficient of material used in the ith layer with thickness of tj

inches.

Deflection of pavement is an indicator of strength of the pavement.

The structural evaluation of pavements mainly comprises of the deflection test which is

a non-destructive test of wide acceptance. Various equipments available for deflection

measurement include Falling Weight Deflectometers (FWD), Steady State Dynamic

Deflection Equipments and Benkelman Beam (Monismith, 1992). Benkelman Beam is

a low cost instrumentation for deflection measurement and is in use in most of the

developing countries (lRC: 81-1981). FWD is comparatively new equipment which is

gaining popularity because of its versatility and speed of testing. The structural

condition data gives insight into the right cause of deterioration and decisions on

strengthening or reconstruction of pavement are made from the analysis of structural

condition data.

2.2.3 Functional Condition Evaluation

Functional condition data is a periodic data which keeps on changing for a

given pavement section with passage of time. Both the riding comfort and Vehicle

Operating Cost (VOC) on a pavement is dependent on the functional condition of the

pavement. The various components of functional condition data are:

i) Different modes of surface distresses

16

ii) Roughness

iii) Skid Resistance

Different modes of surface distresses generally observed on flexible

pavements are cracking, rutting, pothole, edge break etc. These distresses are

quantified by actual measurements. Cracking is measured in terms of type, width and

affected area. Depth of rutting is measured with the help of a wedge and straight edge.

Ravelling, pothole etc. are measured in terms of affected area. The quantity of each

distress is expressed as percentage of the total carriageway area.

Roughness is a major parameter which describes with pavement condition.

Roughness increases with age of pavement. Many a time roughness alone is used as an

indicator of pavement condition. Roughness affects both riding comfort and safety of

the pavement. Increase in roughness causes discomfort to passengers, increase wear

and tear of vehicle and increases Vehicle Operating Cost (VOC). Roughness is usually

measured with Response Type Road Roughness Measurement System (RTRRMS),

. Rod and Level, TRRL Laser Profilometer, Fifth Wheel Bump Integrator and mays

Meter (Hass et ai., 1994; Meyer and Reichert, 1990). The equipment used for

roughness measurement also includes MERLIN (A Machine for Evaluating Roughness

using Low-cost INstrumentation) developed by Transport Research laboratory.

Roughness measured by any of these equipments is later converted to International

Roughness Index (IRI).

Safety of vehicle operation depends on skid resistance of pavement.

Measurement of skid resistance is done with equipment like TRRL Portable Skid

Resistance Tester, Mu Meter, and TRRL Texture Meter etc. (Shahin, 1994).

The TRRL Portable Skid Resistance Tester is the equipment usually used in our

17

country. So far, skid resistance has not been considered as a necessary component of

pavement condition assessment.

2.3 PAVEMENT PERFORMANCE PREDICTION

Functional condition of pavement affects comfort, safety and user cost and the

structural damage of pavements results in functional deterioration. Pavement

performance prediction, both structural and functional in conjunction with other inputs

helps in the formulation of best suited maintenance strategies. Deterioration of a

highway under a set of traffic and climatic conditions can be predicted by the

deterioration models developed for that region.

Pavement performance prediction models are imperative for a complete

Pavement Management System (PMS) (Shahin, 1994). Condition prediction models

are used at both the network levels and project levels to analyse the condition of the

pavements and determine Maintenance and Rehabilitation (M&R) requirements.

At network level, use of prediction models include condition forecasting, budget

planning, inspection scheduling, and work planning. Prediction models are used at the

project level to select specific rehabilitation alternatives to meet expected traffic and

climatic conditions. The models provide the major input to performing Life Cycle Cost

Analysis (LCCA) to compare the economics of various M&R alternatives. When

planning M&R actions at the network level, the concern is normally the level of M&R

needed. At the project level, the concern is focussed on specific M&R alternatives,

including preliminary design of each alternative (Kulkarni and Miller, 2003; Priya,

2008). Therefore, accuracy of prediction is more important for project level analysis

than for network level analysis.

18

2.3.1 Techniques used for Development of Pavement Performance PredictionModels

There are several techniques for developing pavement condition prediction

models (Lytton, 1987; Shahin, 1994; Jon and Martin, 1998). They include straight line

extrapolation, regression, mechanistic-empirical, polynomial constrained least square,

S-shaped curve, probability distribution and Markovian method. Degree of accuracy

required for a model is a function of its intended use.

The simplest condition prediction is based on a straight line extrapolation of

the last two condition points. This method is applicable only for individual pavement

section and does not lead to the development of a model that can be used with other

pavement sections. The method assumes that traffic loading and previous maintenance

level will continue as in the past. The straight line extrapolation method cannot be used

to predict the rate of deterioration of a relatively new pavement or a pavement that has

recently received major rehabilitation.

Regression analysis is used to establish an empirical relationship between two

or more variables. Each variable is described in terms of its mean and variance.

Several forms of regression analysis are used which include linear as well as non-linear

models. Nonlinear regression is a method of finding a nonlinear model of the

relationship between the dependent variable and a set of independent variables. Unlike

the traditional linear regression, which is restricted to estimating linear models,

nonlinear regression can estimate models with arbitrary relationships between

independent and dependent variables. Some of the common nonlinear regression

models are Asymptotic regression, Density, Gauss, Gompertz, Log-Modified, and

Log-Logistic models.

19

A pure mechanistic approach to modelling is applicable only to calculating

pavement response (i.e., strain, stress, and deflection). These responses are caused by

forces due to traffic, climate or a combination of the two. As a pure mechanistic model

cannot be termed as prediction models, the calculated stress, strain or deflection can be

used as input to a regression models for predicting pavement performance. Polynomial

Constrained Least Square technique is one of the powerful techniques for predicting the

change in a variable as a function of one variable. Here pavement condition is

modelled as a function of any factor causing deterioration, like traffic or climate.

A probability distribution describes the probability associated with all the values of a

random variable. The use of probability distribution in predicting pavement condition

requires the knowledge of the distribution law for the variable that is being predicted.

In Markovian technique (Wang et aI., 1994), a pavement condition measuring scale is

divided into discrete intervals called condition states. A duty cycle is taken as the

one year effect of traffic loading, weather or a similar measure. The technique is

based on determining the probabilities associated with pavement in a given condition

state either remaining in that stage itself or either staying in that state or deteriorating

to the next condition state after one duty cycle.

2.3.2 Review of Pavement Performance Prediction Models Developed

The AASHO Road Test (Highway Research Board, 1962) conducted in

USA over a period of two years (1958-60), was the first major land mark, as far as

evaluation of pavement performance is concerned. The test was an accelerated

controlled trafficking experiment on specially constructed pavements. The primary

objective was to determine relationship between number of axle transits of different

loadings and pavement performance. Pavement condition was quantified. in terms of

slope variance, rut depth and cracking and patching area and summarized as Present

20

Serviceability Index (PSI). Pavement strength was expressed in terms of Structural

Number (SN) combining the products of material strength coefficients and thickness of

each layer. The major limitations of the study are:

• The serviceability indices evolved in the study are subjective.

• The tests under accelerated controlled trafficking did not provide any

information on behaviour under mixed traffic conditions.

• The correlations developed are for freezing environment only.

Kenya Study (Hodges et ai., 1975) was conducted by Massachusetts Institute

of Technology (MIT) under the sponsorship of World Bank in 1969. The study was

aimed at the development of a model for the economic evaluation of investment in

roads. First phase of the study was completed in 1971 which developed Highway Cost

Model. Second phase was completed by TRRL, UK and developed Road Transport

Investment Model. Deterioration models were developed for change of roughness,

cracking and rut depth in non-freeze climate for a narrow range of pavement strength,

loading and maintenance standards.

Brazil study (Geipot, 1982) was conducted jointly by Brazilian government

and UNDP and was completed during the period from 1977-82. Prediction models for

progression of ravelling, cracking, potholing, roughness and rutting were developed.

Major limitation of the study was that effect of thick bituminous pavements, low

rainfall, granular base and width of pavement were not considered in the modelling.

Madanat et al. (1998) developed distress progression models from the 111-

service pavement data. Pavement performance prediction models were developed for

Indian conditions by Sood et al. (1996) and Jain et al. (1994). Lee et al. (1987)

developed models to predict serviceability rating as a function of pavement age, traffic

load carried, and Structural Number of pavement. Shahin (1987) used mathematical

curve fitting techniques to predict deterioration of pavement which is capable of

21

operating within a Pavement Management System. Reddy et al. (1997) developed a

model to predict deflection as a function of age, initial deflection and cumulative

standard axle loads.

Central Road Research Institute (CRRI) in the year 1986 on behalf of Ministry

of Surface Transport (MOST) conducted the Pavement Performance Study (PPS) with

a view to develop a long term data base to predict pavement performance. Sood et al.

(1996) extended this study to specially designed and constructed in-service highways

and developed pavement deterioration models especially for Indian conditions using

the data from Pavement Performance Study (PPS). The research project was in two

parts: (i) Study on Existing Pavement Sections (EPS) and (ii) Study on New Pavements

Sections (NPS). I 13 sections of a.5km length were selected for the study. Data

generated from the study included traffic volume, axle loads, climatic conditions,

pavement composition, stress characteristics and subgrade properties. Regression

models were developed for cracking initiation and progression, ravelling initiation and

progression and roughness progression which are shown in Table.2. I and Table 2.2

respectively. Separate models were developed for Pre-mix Carpet (PC), Semi

Dense Carpet (SDC) and Asphaltic Concrete (AC) Pavements. These models have

the capability to enable highway engineers to assess the maintenance requirements of

highway network, project demand for the allocation of resources and to arrive at the

appropriate maintenance strategies. For the easy application of the deterioration

models developed, a software package 'PDM' (Pavement Deterioration Modelling)

was also developed to provide an easy tool for the use of deterioration models. This

package calculates and tabulates the year wise details of different modes of distress and

roughness in future.

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Table 2.1 Pavement Distress Prediction Models (Sood et aI., 1996)

Model Type Model Form N R2 SE

l.PC SurfaceAGECRIN=2.74EXP-.257(CSALYRJMSN2) 22 0.4 0.5

Cracking2. SDC SurfaceAGECRIN=3.29 EXP2.4(CSALYRJMSN2) 28 0.6 0.49

Initiation3.AC SurfaceAGECRIN=4 EXP-l.09CSALYRJMSN2) 20 0.45 0.43

l.PC Surface 212 0.52 0.8CRi/ti= 5.41( CSALYRJMSN)*0.54SCRi 0.28

Cracking 2.SDC Surface

Progression CRi/ti= 5.67( CSALYRJMSN)*0.34SCRi 0.20 98 0.47 0.563.AC SurfaceCRi/ti= 4.26( CSALYRJMSN)*0.56SCRi 0.32 124 0.25 1.14

RavellingAGERVN= 3.18* AXLEYRo. 138*(CQ+l)078 26 0.43 0.38

Initiation

RavellingRVt/ti= 3.94 AXLEYRo.32*SRVi°.46 82 0.28 1.02

Progressionl.PC SurfaceAGEPHIN = 0.21 THBMo.23 *EXP(-0. 18AXLEYR) 13 0.45 0.20

Pothole2.SDC SurfaceAGEPHIN = 0.29THBM°.35 *EXP(-0.22AXLEYR) 21 0.74 0.19

Initiation3.AC SurfaceAGEPHIN = 0.13THBM°.47 *EXP(-0.12AXLEYR) 12 0.75 0.27

Table 2.2 Roughness Progression Models (Sood et aI., 1996)

Model TypeModel Form N RL SE

PC Surface

RG/ =58121(CSAL / SNCK 5 }eO.llPAGE + O.IIRG/ xli248 0.61 252

+184.48~PH, +4.13~CR, +33.46~PT, +9.39~RV,

SDC SurfaceRG( = 39733 (CSAL /SNCK 5)eO.0081 PAGE +0.08R(

Roughness 98 0.43 57Progression + 6.07 !:!.CR / + 1.68 !:!.PT/ + 260.33 !:!.PH /

AC Surface

RG/ =34856(CSAL / SNCK 5 }eO.04PAGE + 0.04RG(

124 0.57 104+ 22.34MT/ +190.57MH( +7.43~CR/

23

where,

N= Number of Observations

R2= Coefficient of Determination

SE= Standard Error

SN= Structural Number of the pavement

CBR= Subgrade strength at the time of pavement investigations

MSN= Modified Structural Number

VDF= Vehicle Damage Factor

CVPD= Commercial vehicles per day

PAGE= Pavement Age since last renewal/strengthening (years)

CRj= Initial cracking(%)

RVj= Initial raveling (%)

PHj= Initial potholes(%)

~CRt= Percent change in cracked area over time '1' in years

~RAVt= Percent change in ravelled area over time '1' in years

~PHt= Percent change in potholes area over time 't' in years

~PTt= Percent change in patched area over time 't' in years

~RGt= Change in roughness over time 't' in year (mm/km)

tj= Time interval(Years)

CSALYR= Cumulative standard axles per year in millions standard axles (msa)

AXLEYR= No. of vehicles axles per year (msa)

THBM= Thickness of bituminous layer (mm)

CQ= Construction quality

0= Good (National Highway)

1= Poor (State Highways)

PC= Pre-mix Carpet surfacing

SDC= Semi- Dense Carpet (Semi-Dense Bituminous Concrete) surfacing

AC= Asphaltic Concrete (Bituminous Concrete) surfacing

Authors have studied the effect of strength of pavement in terms of MSN

and traffic loading in terms of million standard axles (msa) per year on the

initiation and progression of various distresses and roughness for different types

24

of pavement surfaces. Major parameters affecting roughness progression which

include pavement strength, age, traffic loading and initial roughness contribute

about 50% of roughness progression. Based on their extensive data collection,

authors have suggested an initial roughness level of 2500, 1800 and 1500 mm/km

for PC, SDC and AC surfaces respectively. But the construction quality of roads

were subjectively assigned a value of '0' (good) and' 1'(poor) without taking into

account various material characteristics and other construction parameters which

actually affect quality of construction. Further, standardisation of roughness

measurements in terms of IRI was also not attempted in the study.

Reddy et al. (1997) developed flexible pavement deterioration models and

studied the practical application of these models. Data regarding the performance of

flexible pavements on National and State Highways collected over a period often years

as part of a research project was used in the development of deterioration models.

Models developed include deflection growth models (for four ranges of characteristic

deflection), rutting, cracking and unevenness growth models. Models developed are

shown in Table 2.3 and Table 2.4 respectively. The deterioration models developed

were used to predict the performance of different highway pavements during their

design life.

Table 2.3 Deflection Prediction Models (Reddy et al. 1997)

Model FormR2iDEF Range(mm) N SE

0.44 < iDEF <0.61Dt = iDEF + 0.07884 [(Nt * Age) lUter]

28 0.92 0.11

0.66 < iDEF < 0.8Dt = iDEF + 0.0027 exp [(iDEF * Nt)

iDEF] + 0.0859 (Age) 47 0.69 0.29

0.84 < iDEF < 1.05Dt = iDEF + 0.04513 (exp Nt) U.'1) +

45 0.82 0.820.0924 (exp Age)log iDEF

1.10 < iDEF < 1.25Dt = iDEF + 0.03658 [exp (iDEF *

29 0.82 0.2Na]O.5 + 0.19864 (Age)o26

25

Table 2.4 Rutting, Cracking, Unevenness and PSR Prediction Models(Reddy et al. 1997)

Model Type Model Form N R2 SE

(BM + PMC) Surfaced Pavements

Rut depthLog Rdt = 1.67 [iDEF 0.25 + 0.0653 NtJ 103 0.96 0.38(BM + AC Surfaced Pavements

(mm) Log Rdt = 1.367 [iDEF 0.6 + 0.0875 Nt] 158 0.97 0.31

(BM + PMC) Surfaced Pavements 95 0.88 0.52

Rut depthRdt = iRD[1+0.461 (iDEF * Nt)O.62+0.1817 log(Nt)](BM + AC ) Surfaced Pavements

Progression Rdt = iRD[1 +0. 1794(Nt) 1.05 iDEF + 0.0289(Nt)144 0.84 0.3

(BM + PMC) Surfaced Pavements 31 0.98 0.93Crack area Ct = 1.8 [log Nt + 0.1115 (iDEF * Nt)1.48]

(%) (BM +AC) Surfaced PavementsCt = 3.49 [(iDEF *Nl· 34 + 3.24 x 10 -5 exp(Nt) ] 30 0.82 4.06

(BM +PMC ) Surfaced PavementsCAt = iCA[1 +0.744 (iDEF * ND 0.32 +0.0054 exp(Nt)] 27 0.75 0.9

Crack area (BM +AC) Surfaced PavementsprogreSSIOn CAt =iCA [1 +1.49 (iDEF *Nl.l 5+ 0.0547exp (Nt)] 26 0.66 2.9

PSR model PSRt = 14.3765 - 1.9326 Log (UIt) 1074 0.88 0.214

Unevenness UIt = iUI [i + 0.065187 (Nt)I.LL + 0.1843 (DEFo) V.bl

growth Age] 62 0.62 0.233model

where,

iDEF = Initial Deflection (mm)

Dt = Deflection (mm) at time t

Nt = Cumulative Standard axles (millions)

Age = Age of pavement at time '1'

Rdt = Rut Depth (mm) at time '1'

iRd = Initial Rut Depth (mm)

Ct = Crack area (% area affected)

iCA = Initial Crack Area (% area affected)

CAt = Progressed Crack Area in % at time 't'

PSRt = Present Serviceability Rating at time '1'

UIt = Unevenness Index at time '1'

iUI = Initial Unevenness Index

26

A Computer program was also developed which can be used by the practising

engineers to select the best overlay strategy, among different combinations of overlay

materials and thickness duly considering life cycle cost and design life.

Wang et al. (1994) modelled pavement condition deterioration usmg

non homogeneous discrete Markov chain, i.e. at a discrete time, the degradation of

pavement condition is represented by discrete state. Degradation is governed by the

elements of transition matrix which represent probability that pavement performance in

one state deteriorate to another state for a given increment of time index. Since non

homogeneous discrete Markov chain was used, probability transition matrix that

change with time must be assessed for each of the time indices and no specific

mathematical structure was considered for the transition matrix, hence very flexible.

Hong et al. (2003) predicted pavement performance based on a probabilistic

framework. They proposed a stochastic model for the degradation of pavement

performance and used a non-homogenous continuous Markov chain for predicting

pavement performance. Probability transition matrix in this study depends only on two

model parameters, viz., ''A' which control the intensity of transition and 'y' which

affects the time transformation. Measure of pavement performance was done in terms

of Riding Comfort Index (RCI) in Ontario Pavement Analysis of Cost (0PAC) model

and PSI in AASHTO model. This performance measure was divided into 'n' non

overlapping and equal intervals which represented discretized pavement performance.

Each interval denotes a state of pavement performance. The state' n' and' 1' represents

the best and worst pavement performance state. By modelling pavement perfqrmance

as a continuous Markov chain, probability mass function (pmf) of the pavement

performance at a future time can be calculated from the pmf of the pavement

performance at present and the probability transition matrix. The transition probability

27

function from the pavement performance state' i' to the pavement performance state 'j'

for a time increment t, P(i,j(t), i,j ... [1 ,n] satisfies forward Kolmogrov Differ~ntial

equation. But from the study it was seen that the model parameters are relatively

insensitive to the initial pavement condition, which seems contradictory to the research

findings contributed by Sood et al. (1996) and Reddy et al. (1997).

Ortiz-Gracia et al. (2006) proposed three methods for detennination of

transition probabilities. First method was based on the assumption that historical

condition data for each of the site is readily available, the second one utilizes the

regression curve obtained from the original data and the third approach assumes that

yearly distribution of condition data is available. The objective function was to

minimise the difference between each of the method function obtained from the

original data and the corresponding functions obtained from the transition probabilities.

The third method proved to be the best method for detennining the transition

probability matrix.

Thube et al. (2007) conducted a study for the development of PCI based

composite pavement deterioration curves for low volumes roads sections in plain,

rolling and mountainous terrain of India. The deterioration models were developed

using the 'window' methodology, using the pavement perfonnance distress data

collected for two years period on 61 in-service Low Volume Road (LVR) sections.

PCI based pavement deterioration models were developed using nonlinear regression

method in the form PCI=100-K j AGEK2, where K 1 and K2 are constants dependent

upon the maintenance strategy applied and AGE is the age of pavement in months.

Pavement deterioration models developed have been validated also. The comparison

showed an excellent correlation between PCI as predicted by the models and observed

values. The results of the study are useful input for deciding the type and timing of

28

maintenance strategies for LVRs in India. Five maintenance strategies, viz., routine

maintenance, preventive maintenance, rehabilitation, major rehabilitation and

reconstruction and age of roads for triggering these actions were also suggested for

varying levels of PCI.

The model form suggests that, PCI for all roads which are of the same age will

be the same at any point of time. But the condition of road at any time is dependent on

material characteristics and construction parameters apart from its age. Prediction of

depletion of PCI by the proposed model seems to be under estimated. Further basis of

selection of various M&R actions for various levels of PCI are not explained properly.

Henning et al. (2008) developed pavement deterioration models for the State

Highway network in New Zealand based on a Long Term Pavement Performance

(LTPP) data collected from 63 sections of the State Highways. Probabilistic models

developed included a crack initiation model, a three-stage rut progression model i.e.,

initial densification, stable rut growth and a probabilistic model to predict accelerated

rut progression. It has been found that this model type has a strong agreement with

actual pavement behaviour as it recognises a distribution of failure on roads rather than

failure occurring at a particular point in time, namely, a year. Although this research

has covered the two priority pavement models including cracking and rutting

prediction, it has established the model framework for other pavement models to be

developed. Ayed et al. (20 I0) proposed a methodology to develop performance

prediction model in the absence of construction and rehabilitation history. Models

developed use the limited historical data and accounted different parameters like

pavement thickness, traffic, subgrade details and age of pavement. The pavement

sections were classified into 18 performance classes based on this data. A sigmoidal

model was used due to its flexibility in describing the pavement deterioration and a

29

linear programming technique was employed to mll1lmlSe the error involved. The

optimisation included a constraint to limit the service life to the pre-defined expected

service life for each performance class of pavements.

Chen et al. (2011) investigated the applicability of IRI based pavement

deterioration prediction models, which include four deterministic models for pavement

performance prediction (i.e., the NCHRP model, Dubai model, AI-Omari Darter model

and the NMDOT model used by the New Mexico Department of Transportation).

From the comparison of these models with the data from both the NMDOT Pavement

Management System (PMS) and the Long-Term Pavement Performance (LTPP) sites

in New Mexico, it was found that the first two models only fit for pavement

performance prediction in New Mexico. A probabilistic model for pavement service

life prediction, i.e., the survival curve, was also developed. The service life estimated

by the survival curve was compared with those determined from two approaches i.e.,

pavement age and traffic loading and the result of traffic loading approach was found to

be more reasonable.

Sun and Gu (2011) developed a new approach for pavement condition

prediction and project prioritisation integrating the advantages of Analytic Hierarchy

Process (AHP) and Fuzzy Logic theory. Five performance indicators viz., roughness,

deflection, surface deterioration, rutting and skid resistance were used to represent the

pavement condition and the membership functions corresponding to fuzzy linguistic

evaluation set like very good, good, fair, poor and very poor for these performance

indicators were arrived at by survey among experienced professional engineers.

The fuzzy comprehensive evaluation was carried out using fuzzy relations which

combines a single evaluation of single performance indicator to the one considering all

the performance indicators simultaneously. A maximum grade principle and

30

De-fuzzified Weighted Cumulative Index were proposed to assIgn a linguistic

assessment result and numerical assessment result respectively. The proposed method

offers a promising approach to the accuracy and reliability issues in the pavement

condition data collection and prediction.

2.4 PAVEMENT MAINTENANCE AND MANAGEMENT SYSTEM (PMMS)

Pavement Management System (PMS) has been defined in several ways. Hass

and Hudson (1978) defined, "Pavement Management is an all-encompassing process

that covers all those activities such as planning, programming, construction,

maintenance, rehabilitation and research, involved in providing and maintaining

pavements at an adequate level of service". Federal Highway Administration (FHWA,

1988) defined, "Pavement Management System is a system, which involves the

identification of optimum strategies at various management levels and maintains

pavement at an adequate level of serviceability including systematic procedure for

scheduling maintenance and rehabilitation activities based on optimisation of benefits

and minimisation of costs". The concept of PMS started with the AASHO road test

conducted during the period from 1950 to 1960. It was realised during this test that it

would be necessary to evaluate the performance of the pavement in a way that would

be independent of pavement type and that could have universal application for

describing a pavement's condition. A Pavement Maintenance and Management System

(PMMS) is a tool that assists in finding the optimum strategy for maintaining

pavements in serviceable condition over a given period of time with a given budget.

The function of PMMS is to improve the efficiency of decision making, to facilitate co­

ordination of activities within the agency and to ensure the consistency of decisions.

Some of the objectives ofPMMS are:

31

i) To provide the means of developing annual work programmes, requirements and

budgets

ii) To ensure an equitable distribution of funds over the country or the locality and to

enable priorities for allocations to be determined in a rational way when available

funds are inadequate

iii) To authorise and schedule work

iv) To provide a system of monitoring the efficiency and effectiveness of

maintenance works

The work programmes, resources, requirements and budgets identified will

depend upon the type of maintenance procedures used, the frequency with which these

are carried out, and the size of the management and organisational overhead needed to

support the maintenance activity. The system will also provide functional data to

support the budget requests. These data will be obtained as a result of determining

maintenance needs using quantitative field inspections and monitoring work completed

to ensure that it has been carried out in a cost effective manner.

2.4.1 Levels of Pavement Management

Various activities in a PMMS, including decision making have been

categorised into network level and project level (Haas, 1978). The network level can

be further divided into the project selection level and the programme level. The project

selection level involves prioritisation to identify which projects should be carried out in

each year of the programme period. The project selection level involves decisions on

funding for projects or groups of projects as opposed to the program level which

involves general budget allocation decisions for an entire highway network.

Programme level, involves policy decisions regarding rehabilitation or maintenance for

the network as a whole. At this level, allocation of budget is the major concern, and the

32

models should be designed to optimise the use of funds allocated to rehabilitation and

maintenance.

PMS models at project level deal with detailed design decisions for individual

projects and they require detailed information of sections of pavement. The inputs for

project level model include traffic loads, environmental factors, material and subgrade

characteristics, construction and maintenance details and costs. The models at project

selection level are geared to less detailed data for a set of projects under consideration

and involve prioritisation models based on optimisation or other techniques under

budget constraint condition. Models at programme level should be designed to

optimise the use of funds allocated for rehabilitation and maintenance and for this, data

regarding existing condition of the whole network is needed so that the effect of

rehabilitation and maintenance policies can be evaluated.

2.4.2 Soft Computing Techniques used in PMMS

Many researchers have exploited the advantages of the soft computing

techniques in PMMS.

2.4.2.1 Artificial Neural Networks

Neural Networks are simplified models of the biological nervous system and

therefore have drawn their motivation from the kind of computing performed by a

human brain (Flintsch and Chen, 2004). An artificial neural network is a highly

interconnected network of large number of processing elements called neurons in an

architecture inspired by the human brain. In most cases an ANN is an adaptive system

that changes its structure based on external or internal information that flows through

the network during the learning phase. A neural network can be trained to perform a

particular function by adjusting the values of the connections (weights) between

elements so that a particular input leads to a specific target output as shown in Fig. 2.1.

33

Input

Neural Network_~~ including connections

(called weights)between neurons

Adjustweights

Output

Fig. 2.1 Working of Neural Network (Beale et aI., 1992)

Data are presented to the input layer and the response (results) of the network

is held in the output layer. Intermediate layers are referred to as hidden layers which are

introduced between input and output layers so as to model a complex phenomenon

which is known as multi-layer concept. The numbers of units in the input and output

layers are fixed by design whereas the number of units in the hidden layer is generally

determined by trial and error method. A neuron with a single R-element input

vector is shown in Fig. 2.2. Here the individual element inputs PI, P2... PR is

multiplied by weights W II , W 12, .... W IR, and the weighted values are fed to the

summing junction. Their sum is simply W.p, the dot product of the (single row)

matrix Wand the vector p. The abbreviated notation of a single neuron is shown

in Fig. 2.3.

Input,.......1

Neuron

\

1

o ""...._'_., .....)a= f(Wp +b)

INhere

R= number ofelements Ininput vector

Fig. 2.2 Single Neuron using Abbreviated Notation(Beale et aI., 1992)

The neuron has a bias b, which is summed with the weighted inputs to form

the net input n. This sum, n, is the argument of the transfer function 'f. As shown in

Fig. 2.3, the input vector 'p' is represented by the solid dark vertical bar at the left.

34

The dimensions of 'p' are shown by the symbol p in the Fig. 2.2 as Rx 1. This input

vector is multiplied by the weight matrix, Wand a constant 1 enters the neuron as an

input and is multiplied by a scalar bias 'b' A layer includes the combination of the

weights, the multiplication and summing operation (here realised as a vector product

W.p), the bias b, and the transfer function f. The weight matrix connected to the input

vector is labelled as an input weight matrix, IW with the subscripts representing the

source and destination and the weight matrix attached to the hidden layers is labelled as

layer matrix, LW. Each layer has a weight matrix W, a bias vector b, and an output

vector a. The outputs of each intermediate layer will be the input to the following

layer.

Input,--.... (

Hidden Layer

\ (Output Layer

a~ +l~~ \.,-_~_X_l -"

nl =tmlSig rfWl.lp' +bli

J xl 3I )'"'--------".

Fig. 2.3 Two Layer Network using Abbreviated Notation(Beale et aI., 1992)

The three transfer functions mainly used in ANN are Purelin, Tan-sigmoid and

Log-sigmoid the notation of which are shown in Fig.2A.

I

Purelin

···..·..···i?':n 0:2h=

a = tar<sg(n)

Tan-sigmoid

a

2::~n CQ

Log-sigmoid

Fig. 2.4 Representation of Transfer Functions(Beale et aI., 1992)

Feed forward networks often have one or more hidden layers of sigmoid

neurons followed by an output layer of linear neurons. Multiple layers of neurons with

35

nonlinear transfer functions allow the network to learn nonlinear and linear

relationships between input and output vectors. Back propagation was created by

generalising the Widrow-Hoff learning rule to multiple-layer networks and nonlinear

differentiable transfer functions. Input vectors and the corresponding target vectors are

used to train a network until it can approximate a function, associate input vectors with

specific output vectors, or classify input vectors in an appropriate way as defined by the

user. The two back propagation algorithms commonly used are batch gradient

(TRAINGD) and batch gradient descent with momentum (TRAINGDM).

Modelling using Neural network can be done using neural network tool

box available in MATLAB (Pratap, R., 2010). The Graphical User Interface

(GUI) is a tool used to work with the neural network which facilitates to create

network, enter the data, train and simulate the networks.

Steps involved in GUI are:

a) Input and Target

c) Training the Network

2.4.2.2 Fuzzy Logic

b) Creation of New Network

d) Simulation of Network

Problems of the real world featuring complexity and ambiguity have been

addressed sub concisely by humans because they could think (Ross. T. 1., 1997;

Pratap, 2010). For systems with little complexity, hence little uncertainty, closed form

of mathematical expression provides precise description. For most complex systems

where few numerical data exit and where imprecise information may be available,

fuzzy reasoning provides a way to understand the behaviour. Fuzzy logic is a

convenient way to map an input space to an output space. It is a methodology for

handling of inexact, imprecise, qualitative information in a systematic and rigorous

way. The underlying power of fuzzy is that it uses linguistic variables rather than

36

quantitative variables to represent imprecise concepts. Fuzzy logic seems to be the

most successful in two kinds of situations:

i) very complex models where understanding is strictly limited.

ii) process where human reasoning, human perception or human decision-making is

involved.

Computer logic is binary with two values 0 and 1. The binary pair {O, I} can

be well expressed in such term as {no, yes}, {off, on}, {low, high}. This type of

universe of quantities, which can be expressed as either of two values is described as a

'crisp' universe. However in a man machine system, there arises the problem of

processing information with the vagueness that is characteristic of man. For example a

human will answer not only 'yes' or 'no' but also 'almost yes', or 'don't know',

'somewhat', 'slightly' etc. Here lies the importance of fuzzy logic, which provides a

mathematical way to represent vagueness in human system. Prof. Lotfi Zadeh of

university of California was the first to realize the concept of fuzzy in 1965.

Membership function in fuzzy logic is a curve that defines how each point in

the input space is mapped to a membership value (degree of membership) between 0

and 1. For example, all people taller than 6 ft. are officially considered tall. But such a

distinction is clearly absurd in reality since it is unreasonable to call one person short

and another tall when they differ in height by a width of a hair. 6 ft. and 5 ft. 11 inches

are tall to some degree, but one is significantly less than other. This can be shown by

drawing a curve with height on x-axis and degree of membership on y-axis. The

degree should vary from 0 to 1. The simplest membership functions are formed using

straight lines and among these the simplest is the triangular membership function.

Trapezoidal membership function has a flat top and really is just a truncated triangular

37

curve. Other membership functions include Gaussian curve, Sigmoidal etc. Various

types of Membership Functions are shown in Figs. 2.5 to 2.8.

1.00

Q)::l~ 0.80

>0-:.a 0.60

~Q)

..0 0.40

EQ)

~ 0.20

0.00 2.00 ".00 8.00 8.00 10.00

Fig. 2.5 Triangular Membership Function (Ross, T.J., 1997)

, 0

~ 118

o:l

>0.•0-

:aen... o.Q)

.DEQ)"

:::8

7.00 A.OO 'O.OD

Fig. 2.6 Trapezoidal Membership Function (Ross, T.J., 1997)

Q) 1::l-a> 0.i50-

~ 05...Q)

.D 025EQ)

:::8 0

o 10

Fig 2.7 Gaussian Membership Function (Ross, T.J., 1997)

Q)

::l-a 0.15>0-

:a osen...Q) Q.2;

.DEQ) 0

:::8 0 2 G 8 10

Fig 2.8 Sigmoidal Membership Function (Ross, T.J., 1997)

38

2.4.2.3 Genetic Algorithm (GA)

Genetic Algorithm (GA) is an evolutionary computing technique that actually

mInImISeS the mechanism of natural selection process. The concept of GA was

developed by Holland and his colleagues in the 1970s (Goldberg, 1989). GAs is

inspired by the evolutionary theory explaining the origin of species. In nature, weak

and unfit species within their environment are faced with extinction by natural

selection. The strong ones have greater opportunity to pass their genes to future

generations via reproduction and in the long run, specIes carrying the correct

combination in their genes became dominant in their population. Sometimes, during

the slow process of evolution, random changes may occur in genes. If these changes

provide additional advantages in the challenge for survival, new species evolve from

the old ones.

GA differs from the classical, calculation based optimisation technique in the

following ways (Goldberg, 1989; Yin, 2000):

(i) GA searches simultaneously from a population of points known as chromosomes

to explore the solution space

(ii) GA uses probabilities transition rules through its operators for the search of

solution space with the expectation of successive improvement

(iii) GA works smoothly with both continuous and discrete parameters and both

differentiable and non-differentiable functions.

In GA terminology, a solution vector x € X is called an individual or a

chromosome. Chromosomes are made of discrete units called genes. Each gene controls

one or more features of the chromosome. In the original implementation of GA by

Holland, genes are assumed to be binary digits. In later implementations, more varied

gene types have been introduced. Normally, a chromosome corresponds to a unique

solution x in the solution space. GA uses mainly three basic operators to generate new

solutions from existing ones viz., a) Reproduction, b) Crossover c) Mutation.

39

a) Reproduction

Reproduction or Selection, the first genetic operator applied on a population,

makes more copies of better strings to form a new population for the next generation.

In the general case, the fitness of an individual determines the probability of its survival

for the next generation. There are different selection procedures in GA like

'Proportionate Selection' (Roulette Wheel Selection) and 'Ranking and Tournament

Selection' depending on how the fitness values are used. Reproduction ensures that

better stings are selected from the current population and their multiple copies are

inserted into the mating pool in a probabilistic manner.

b) Crossover

A crossover operator is used to recombine two strings to get a better string and

the recombination process creates different individual in the successive generations by

combining genes from two individual solutions of the previous generation. In the

crossover operator, new strings are created by exchanging information among strings of

the mating pool. The crossover operator is the most important operator of GA. In

crossover, generally two chromosomes, called parents, are combined together to form a

new chromosome, called 'offspring'. The parents are selected among existing

chromosomes in the population with preference towards fitness so that offspring is

expected to inherit good genes which make the parents fitter. By iteratively applying

the crossover operator, genes of good chromosomes are expected to appear more

frequently in the population, eventually leading to convergence to an overall good

solution. In order to preserve some of the good strings that are already present in the

mating pool, all strings in the matting pool not used in the crossover process. When a

crossover probability, defined here as 'Pc' is used, only (l00 x Pc) percent strings in the

population are used in the crossover operation and 1OO( 1-Pc) percent of the population

40

remains as they are in the current population. Many crossover operators exist in the

GA literature. One site crossover and two site crossover are the most common ones

adopted (Liu, et al. 1997). In crossover operations, two strings are picked from the

matting pool at random and some portion of the strings is exchanged between them. In

one site cross over process, one site is selected at random and the bits on the right side

of the site are exchanged. Two site crossovers is a variation of the one site crossover,

except that two crossover sites are chosen and the bits between the sites are exchanged.

A one site crossover is shown in Fig. 2.9 by randomly choosing a cross over site along

the strings and by exchanging all bits on the right side of the cross over site. A

crossover site is selected randomly (shown as vertical lines) and the portion right of the

selected site of these two strings are exchanged to form a new pair of strings. The new

strings are thus a combination of the old strings.

B B

10 11 12 13 14 15

B B B

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Fig. 2.9 Crossover Operation

where A & B are two maintenance strategies considered.

c) Mutation

The mutation operator introduces random changes into the characteristics of

chromosomes. Mutation is generally applied at the bit (gene) level. In typical GA

41

implementations, the mutation rate (probability of changing the properties of a gene) is

very small and depends on the length of the chromosome. Therefore, the new

chromosomes produced by mutation will not be very different from the original one.

Mutation plays a critical role in GA and reintroduces genetic diversity back into the

population and assists the search escape from local optima. Mutation adds new

information in a random way to the genetic search process and ultimately helps to

avoid getting trapped at local optima. It is an operator that introduces diversity in the

population whenever the population tends to become homogeneous due to repeated use

of Reproduction and Crossover operator.

When the bits are being copied from the current strings to the new strings, there

is probability that each bit may become a mutated. But this probability is usually a

quite small value, called as mutation probability 'Pm' which is used to decide the

number of bits to be muted. A coin toss mechanism is employed to exercise mutation,

i.e., if the random number generated between '0' and' l' is less than the mutation

probability, then only the bit is randomly changed. This random scattering should

result in better optima, or even modify a part of genetic code that will be beneficial in

later operations. On the other hand, it may produce a weak individual that will never

be selected for further operations. The need for mutation is to create a point in the

neighbourhood of the current point, there by achieving a local search around the current

solution. Simple genetic algorithm generally uses mutation rate between 0.001 and 0.5.

2.4.3 Highway Development and Management Tool (HDM-4)

The Highway Design and Maintenance Standards Model (HDM-3), developed

by the World Bank, has been used for over two decades to combine technical and

economic appraisals of road projects, to prepare road investment programmes and to

analyse road network strategies. The Road Deterioration and Maintenance Effects

42

models of HDM-3 were developed from long term performance studies conducted

primarily in Brazil and widely verified on independent field studies from several

different countries.

2.4.3.1 Road Deterioration and Maintenance Effect Models in HDM-4

The International Study of Highway Development and Management

(ISOHDM) has been carried out to extend the scope of the HDM-III model, and to

provide a harmonised systems approach to road management, with adaptable and user-

friendly software tools (Final Report of ISOHDM, 1995). This led to the development

of Highway Development and Management Tool (HDM-4). Deterioration models

developed in HDM-4 are shown in Table 2.5.

Table 2.5 Road Deterioration and Work Effect Models of HDM-4

(Final Report of ISOHDM, 1995)

Parameter Model Form

lRV = Kvi (aD exp(-a1 CDR - a2 VAX))

Ravelling InitiationCDR = CORe + CDRde

CDR = max(CDRci) + max(CDRdei)

Ravelling Progression6ARV=Kvp-' Zr{ [Zraoal6TRV+SRVa

l] lIa, -SRV}

[ a1+a2HS ]Pothole Initiation IPT = Kpi aD (a3 + CDB)(aS + MMP)

LiNPTj

_ [(a3 + CDB)(a4 + VAX) (as + MMP)]- KppaDADlS j a1 + a2 HS

Pothole Progression 3

LiNPT =I LiNPOTjj=l

VEB - Keb aD PSH AADT 2 ESTEP Sal (a2 + MMP)-Edge Break Progression PSH = max[min(a3 - a4CW, i), D)

Roughness ProgressionLilRI = kD(k1LilRIs + k2LilRIe + k3LiIRIv + k4LiIRIr

+ kSLilRIt + k6LiIRId + k7LilRIh + k8LilRIe

43

where,

CDR = Construction Defects factor for Ravelling

AO to a5 and kO to k8 = Parameters depend on the pavement type

CDRc

CDRde

~ARV

Kvp

Zr

~TRV

SRV

IPT

Kpi

CDB

MMP

HS

~NPTi

ADISi

Kpp

VEB

PSH

AADT

ESTEP

S

Keb

MRIs,

MRIc,MRIv

~IRIr, MRIt,

~IRId, MRIh,

MRIe

= Largest defect factor due to construction defects

= Largest defect factor due to Design and EnvironmentalConditions

= Percentage change in Ravelled area in the Analysis Year

= Ravelling Progression Factor, default is 1

= is 1, if ARVa < 50; else =-1

= is 0, if AGE2< IRV and ARVa = 0, is (AGE2-IRV), if(AGE2-1) <IRV:::;AGE2 and ARVa = 0

= is min (ARV, 100-ARV)

= Time between cracking or ravelling or initiation of Potholes

= Pothole Initiation Factor

= Construction Defects Indicator for the Base

= Average Rainfall in m/month

= Asphalt Thickness in mms

= Additional number of Potholes Per krn due to the distress type1

= Percent Area of Indexed Cracking

= Pothole Progression Factor

= Annual Loss of Edge Material in m2/km

= Proportion of Time Using Shoulder

= Annual Average Daily Traffic

= Elevation Difference from Pavement to Shoulder

= Average Traffic Speed in km/hr

= Edge Break Progression Factor

= Structural Component of IRI Increment

= Component of IRI Increment due to Cracking and Ravelling

= Component of IRI Increment due to Rutting, Potholing

= Component of IRI Increment due to Delamination andPatching

= Environmental Component of IRI Increment

44

2.4.3.2 Applications of HDM-4

Analysis tools of HDM -4 include:

a) Strategy Analysis

b) Programme Analysis

c) Project Analysis.

HDM-4 can assist with project analysis for detailed economic appraisal,

programme analysis for annual or rolling work programme preparation, strategy

analysis for long term planning and research and policy studies.

a) Strategy Analysis

The concept of strategic planning of medium to long term road network

expenditures requires that a road organisation should consider the requirements of its

entire road network asset. Thus strategy analysis deals with entire networks or sub­

networks managed by one road organisation. Typical applications of strategy analysis

by road administrations would include medium to long term forecasts of funding

requirements for specified target road maintenance standards and forecasts of long term

road network performance under varying levels of funding.

b) Programme Analysis

This deals primarily with the prioritisation of a defined long list of candidate

road projects into a one-year or multi-year work programme under defined budget

constraints. Road networks are analysed section by section and estimates are produced

of road works and expenditure requirements for each section per year during the

funding period.

c) Project Analysis

Project analysis is concerned with the evaluation of one or more road projects

or investment options. This application analyses a road link or section with user­

45

selected treatments with associated costs and benefits projected annually over the

analysis period. Economic indicators are estimated for the different investment options.

2.4.3.3 HDM-4 Modules

Modules of HDM-4 on which the three analysis tools (Strategy, Programme

and Project) operate are:

• Road Network: Defines the physical characteristics of road sections in a network

or sub-network to be analysed.

• Vehicle Fleet: Defines the characteristics of the vehicle fleet that operate on the

road network to be analysed.

• Works Standards: Defines maintenance and improvement standards, together

with their unit costs, which will be applied to the different road sections to be

analysed.

• HDM Configuration: Defines the default data to be used in the applications.

A set of default data is provided when HDM-4 is first installed, but users

should modify these to reflect local environments and circumstances. Technical

analysis within the HDM-4 is undertaken using four sets of models namely RD (Road

Deterioration) which predicts pavement deterioration for bituminous, concrete and

unsealed roads, WE (Works Effects) which simulates the effects of road works on

pavement condition and determines the corresponding costs, RUE (Road User Effects)

which determines costs of vehicle operation, road accidents and travel time and SEE

(Social and Environment Effects) which determines the effects of vehicle emissions

and energy consumption.

2.4.3.4 Calibration of HDM-4 Deterioration Models

Application of HDM-4 deterioration models involves two important steps viz.,

i) data input which includes a correct interpretation of the data input

requirements, and achieving a quality of input data that is appropriate to the

desired reliability of the results

46

ii) calibration of outputs by adjusting the model parameters to enhance how well

the forecast outputs and observed outputs represent the changes and

influences over time and under various interventions.

Calibration of the HDM model focusses on the two primary components that

determine the physical quantities, costs and benefits predicted for the analysis, viz.,

Road User Effects (RUE) which is comprised of vehicle operating costs (VOC), travel

time, safety and emissions, and Road Deterioration and Works Effects (RDWE), which

is comprised of both the deterioration of the pavement and the impact of maintenance

activities on pavement condition and the future rate of pavement deterioration.

Calibration of HDM-4 to Indian conditions was done by Roy et al. (2003).

The methodology adopted for calibration was simple and straight forward. The

deterioration factors for respective distress modes have been varied and the

deterioration predicted for the pavements under different traffic loading conditions and

structural number was obtained. This was compared with the prediction done by PPS

model for the same loading and structural and functional conditions. The deterioration

factors which gave closer relationship with the PPS model predictions were selected as

required calibration factors. The statistical significance of this selection was

ascertained using Chi-squared test. For comparison of prediction of pavement distress

between PPS and HDM-4, following compositions were used in the study:

i) Bituminous Concrete surfaced pavement with Surfacing 25mm BC, DBM 75mm,

BM 75mm, Base: WBM and Sub base: Granular ii) Single Bituminous Surface

Dressing (Premix Carpet), Surfacing : Premix Carpet 20mm, Base: WBM, Sub base:

WBM.

HDM-4 Pavement Deterioration Models have been calibrated for Indian

National Highway Network in a study conducted by Jain et al. (2005). Calibration of

47

HDM-4 is intended to improve the accuracy of predicted pavement performance and

vehicle resource consumption. The pavement deterioration models incorporated in

HDM-4 were developed from results of large field experiments conducted in several

countries. Consequently, the default equations in HDM-4, if used without calibration,

would predict pavement performance that might not accurately match the observed

values of road sections. In their study, the predictions made by the Indian deterioration

models developed under the PPS-EPS and the corresponding HDM-4 road deterioration

models have been equated under the same set of data conditions, and the respective

calibration factors have been determined. The validity of the calibrated pavement

deterioration models was checked on a selected national highway network, and the

efficacy of these models were tested.

Roy et al. (2006) conducted a sensitivity analysis of input parameters for

application of highway development and management tool (HDM-4) for investment

decisions. In their study, 40 parameters were considered for sensitivity analysis and

each parameter was given a grid of possible values by providing 10 equally spaced

levels between first and last value of the range. From the 400 combinations, 200 cells

were randomly selected. HDM-4 model was run for each of the 200 parameter

combinations in the experimental plan. The output chosen for the case study was the

Economic Internal Rate of Return (lRR) of life cycle cost analysis for a period of 30

years. A first order linear regression model relating the 40 input variables to the IRR

values from the 200 runs ofHDM-4 was developed using SPSS (Statistical Package for

Social Sciences). From the sensitivity analysis of the 40 parameters selected, it was

found that the most significant factors affecting the IRR value was carriageway width

followed by pothole initiation calibration factor and finally skid resistance. Authors

tried to explore all practical factor ranges of the input space. The major limitation of

48

the study was that it did not deal with the non-linear interaction between input variables

which would have been more realistic.

Calibration ofHDM-4 for Rural Roads in India was done by Jain et al. (2007).

In their study they made an attempt to calibrate HDM-4 pavement deterioration models

for rural roads in India by using the "window" monitoring techniques which consist of

reconstructing the distress performance curve of a specific road category starting with

observation of the condition of different roads with similar characteristics but of

different ages. Road sections selected for the study were categorized into nine cells

consisting of homogeneous sections based on traffic, terrain and age. Range of

calibration factors proposed by the authors for various distresses and roughness are

shown in Table 2.6. The process of calibration consists of determining the adjustment

factors which will achieve the best agreement between the model's prediction and the

field data. Different trial calibration factors were attempted for road sections and the

calibration factor corresponding to the maximum coefficient of determination (R2

value) and minimum Root Mean Square Error (RMSE) value was suggested amongst

the different trial calibration factors.

Table 2.6 Calibration Factors for Rural Roads (Jain et al. 2007)

Criteria Initiation Progression

Cracking 0.5 -1.28 0.2 - 0.27

Ravelling 0.062 - 0.08 0.17-0.54

Edge Break 0.14 - -0.17 -

Rut depth - 1.4 - 2.2

Pot hole I 0.02

Roughness - 2.1 - 2.6

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Rural roads selected for the study is supposed to have low axle loads, but still

the rate of rut depth progression is fairly higher than that in HDM-4. Further, rate of

ravelling and pothole progression is low which seems contrary in the case of Pre-Mix

Carpet surfacing which is the normally provided surfacing for rural roads.

2.4.4 Pavement Maintenance Treatment Decision Approaches

Maintenance is defined as a set of well-timed and executable activities to

ensure and prolong pavement life, until the pavement deteriorate to the minimum

acceptable level and the rehabilitation of pavement proves to be more cost-effective.

Periodic maintenance of road section is very important for its optimal use. Various

tools are available for the prioritisation of roads in a network for periodic maintenance.

Benmaamar et al. (2003) developed a ranking method for the prioritisation of

low volume roads in Tanzania. This ranking method can be used as an alternative to

the consumer surplus method used by HDM-4. The consumer surplus method is

generally considered reliable when applied to high volume roads (AADT>200).

Its application to low volume roads encounters problems related to the small magnitude

of user benefits and the stronger influence of the environment rather than the traffic on

road deterioration. So a cost effective approach that takes into account of the social

and economic importance of rural infrastructure interventions is to be applied to

prioritise investments. Establishing the priorities for rural road interventions In

Tanzania required a selection process consisting of a combination of screening and

ranking procedures. The screening process reduced the number of investment

activities. This was done by targeting disadvantaged communities based on poverty

indices using the Human Development Index by region. After applying screening

methods to a given set of investment choices, resources were unlikely to be sufficient to

finance the balance of the remaining desirable interventions, and hence a ranking or

50

prioritisation method based on Cost EfTectiveness Analysis (CEA) was developed.

Cost Effectiveness Indicator of a link is the ratio of cost of upgradation of a link to a

basic standard to the population served by that link. A threshold CE-value was

determined below which a link should not be considered for investment. Unlike the

conventional Cost-Benefit analysis, this approach seems to be easier to implement and

requires no traffic data for each road link. The population of the catchments area of

each road link can be used as a proxy variable to estimate the project benefits and

hence these data should form a part of the data base.

Chandran et al. (2007) has done the prioritisation of low-volume pavement

sections for maintenance using Fuzzy Logic. The success of a Pavement Management

System depends on the pavement condition data and accuracy of prediction of

pavement performance. Reliability of subjective rating techniques used to study the

pavement performance is poor due to the subjectivity associated with it. To deal

effectively with the subjectivity associated with human judgment of distress severity

and extent, mathematical techniques of fuzzy sets were used. Fuzzy membership

functions were formulated for severity, extent and relative importance of each distress

with respect to maintenance. Fuzzy condition indices were used to prioritise the

pavement sections by suitable fuzzy ranking methods. A detailed functional

performance assessment was carried out on the same sections. The pavement sections

were also prioritised based on the PCI values and a comparison of both methods was

done and the effectiveness of the methodology was established except for minor

variations.

Maintenance decisions can be based on static or dynamic models. A static

decision model assumes that the time at which the pavement performance falls below a

minimum value can be predicted with certainty and hence timing of future M&R

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actions can be determined at present (Kulkarni, 1984). But a dynamic model uses a

stochastic deterioration model which predicts probabilities of different pavement

condition and an appropriate choice of M&R action for each condition is made. The

approaches for arriving at the maintenance decisions can be classified as:

i) Heuristic approach

ii) Scenario based approach

iii) Approach which uses some optimisation technique.

Heuristic approach is based on subjective judgment and past experience, the

condition of pavement is represented by a composite index or as a measured parameter

and maintenance decisions are made by engineering judgment (Hicks et a1. 2000).

Ashraf and George (2000) conducted a study for pavement maintenance strategy

selection using Artificial Neural Network (ANN). A Genetic Adaptive Neural Network

training algorithm with single hidden layer and sigmoid squashing function was

selected as the network. Input vectors represented the factors affecting maintenance

strategy selection like the condition of road, traffic volume, class of road etc., while the

output vector represented the appropriate maintenance strategy. Six maintenance

strategies considered include do nothing, surface seal coat, overlays of three varying

thicknesses and reconstruction. The trained network successfully predicted 83 percent

of the test cases and the remaining 17 percent were 1 or 2 levels away from the expert

judgments which were used for network testing. So ANN can be an effective tool,

when a good set of training data is available, since the system can learn enough

information and function better than an expert system. Authors concluded that neural

network provide an efficient and optimum solution for such complex problems with the

added advantage of faster implementation and easier updating than other traditional

techniques.

52

A study conducted by Alsugair ct al. (1998) used ANN to determine the

appropriate maintenance and repair option that must be applied to road sections in a

network. Each training set for the ANN model consists of the input data, i.e., the

pavement condition represented by the PCI value of the pavement and the output data,

which are appropriate M&R actions. Pavement condition data used in their study were

obtained from comprehensive visual inspection data conducted on the Riyadh road

network in Saudi Arabia. The associated M&R actions were obtained by consulting

human expertise and from M&R action recommended by Pavement Maintenance

Decision Support System (PMDSS) software.

Scenario based approach is based on engineering economic analysis which

encompasses a broad collection of techniques for selection, evaluation and

recommendation and prioritisation of investment options. Life Cycle Cost Analysis

(LCCA) is based on the principles of economic analysis to evaluate the overall long­

term efficiency between competing investment options. The Highway Development

and Management Tool (HDM-4) is a scenario based approach which uses the

incremental benefit-cost ratio method or Internal Rate of Return (IRR) method to arrive

at the suitable maintenance decisions at the project level (Kerali and Mannisto, 1999;

Capuruco and Tighe, 2003; Sharma and Pandey, 1997). Road Development and

Maintenance Investment Decision Models developed based on Indian conditions

incorporated updated Vehicle Operating Cost relationships and pavement performance

and deterioration prediction relationships for Indian conditions. It enables the

engmeers to make a more accurate evaluation of economic benefits of highway

schemes. Practical application of these models include: i) evaluation of economic

effect of road maintenance and setting maintenance strategies ii) economic evaluation

53

of highway schemes relating to widening of carriage way, pavement strengthening, bye

pass project and iii) evaluation of financial viability of highway projects.

Prioritisation of Road Maintenance Operation Using HDM-4 under various

budget scenarios was done by Singh and Sreenivasulu (2005). The method of

optimisation selected was an incremental analysis and considered all options with

higher discounted total economic cost, and compared these incrementally against the do

minimum option. The procedure selects the maximum NPVIcost ratio options that

would be within budget limit. An incremental search technique was used to select the

options starting with the highest incremental NPVIcost ratio, ensuring that at any time

there is not more than one option selected per road section. The process continues until

the budget is exhausted for each budget period. Thus it was found that using HDM-4

model, optimum utilisation of the meager funds and prioritisation of maintenance

works under different budget scenarios are possible.

Tsunokava and Islam (2003) studied the relationship between optimal

pavement design, maintenance strategy and the Level of Economic Development

(LED). It was found that pavement strategy should be more economical in developing

countries both for initial design and subsequent maintenance. The extent to which the

pavement should be constructed stronger, in order to counteract the insufficient

maintenance practice was also quantified. Based on the analysis a formula that predicts

optimal pavement strength as a function of axle loading, LED and level of maintenance

insufficiency was arrived at and it was concluded that HDM-4, if properly calibrated is

an appropriate design tool for pavements.

The Roads Economic Decision (RED) Model was developed by World Bank

(World Bank, 2004) with the intention to use it in decision making process for the

development and maintenance of low volume roads. The model performs an economic

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evaluation of road investments using the consumer surplus approach which measures

the benefits to the users and consumers of reduced transport costs. The model is

customized to the characteristics and needs of low volume roads such as high

uncertainty of the assessment of model inputs, especially the traffic and condition of

unpaved roads, the importance of vehicle speeds for model validation, the need for a

comprehensive analysis of generated and induced traffic and the need to define benefits

attained. RED model computes benefits for normal, generated, induced and diverted

traffic and takes into account changes in road length, condition, geometry, type,

accidents and days per year when the passage of vehicles is further disrupted by a

highly deteriorated road condition. The model is presented on a series of Excel 2000

worksheets that collect all user inputs, present the results in an efficient manner and

performs sensitivity, switching values and risk analyses.

Dhaliwal et al. (2004) proposed the need to address the typical requirements

of a PMS suitable for the fast developing Indian Highway Network. As per the study,

the effectiveness and major functions of applying a PMS in an actual network depends

not only on its structural components but also on the particular agency for which it is

implemented. Since the management system and its corresponding organisational

structure in India are much different from most of other countries, PMS which suits a

special situation of the country was to be designed. Their study concluded that such a

PMS should have the following characteristics (i) it should be able to operate

efficiently at both network and project level (ii) it should be applicable with

modifications and be effective in large agencies (iii) it should possess sufficient

flexibility and (iv) it should be capable to make good use of the existing and new

technology.

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Gedafa (2006) conducted a study to highlight the present pavement

maintenance practice around the world with a particular attention to the maintenance

trend in India and use of Highway Development Management Tool (HDM-4) for the

maintenance of a section in Mumbai Metropolitan Region (MMR). This region has a

humid, warm and wet climate prevalent in the west coast of India. Condition

responsive maintenance has been carried out and cracking and roughness only have

been found out to be critical. It was also found that condition responsive maintenance

was better than time bound (scheduled maintenance) and the rate of deterioration due to

cracking was higher than that of roughness.

Optimisation techniques makes use of rigorous tools that are capable of giving

best solution there by assisting the decision making process (Chan et aI., 1994; Ferriera

et aI., 2002; Fwa et aI., 2000; Chan et aI., 1994) demonstrated the applicability of

Genetic Algorithm as an optimisation tool for the road maintenance planning problem

at network level. A computer model PAVENET, formulated on the operating

principles of genetic algorithms to serve as an analytical aid for pavement engineers

were developed. Only preventive or corrective maintenance activities were considered

excluding major rehabilitation actions during the analysis period. The planning period

was split into an active period (period during when both preventive and corrective

actions are done) and a passive period (period during when only corrective actions are

done). Problem parameters were coded in binary form for small problems and non­

binary form for larger ones. The objective function in PAVENET can be user defined,

i.e., to minimise present cost of maintenance activities over the planning period or to

maximise usage of yearly allocated budgets. Three distress types, viz., cracking,

rutting and surface disintegration were considered in the study. Effect of pavement age

structure, warning levels of various distresses considered in the study, and budget

56

allocation were studied in the analysis. Authors concluded that a maintenance

programme should always be planned such that the maintenance demand pattern is

gradually transformed into one that is uniformly distributed.

Isaac and Veeraragavan (1996) developed software named PADMA

(Pavement Deterioration and Maintenance) and tried to bring out the power of expert

system as a decision support system for maintenance of roads. PADMA can function

as an expert in deciding the cause of the distress in flexible pavements and in selecting

the most appropriate maintenance treatment to be carried out. This program was

developed using the expert shell, DEKBASE. Expertise has been drawn from practices

and guidelines followed by various agencies and personal discussion with engineers,

researchers and academicians. Program identifies the type of defects and causative

factors and recommends the most appropriate treatment.

Wang and Liu (1997) developed a Network Optimisation System (NOS)

model with the objective of maximising pavement performance. In order to rule out the

wide disagreement in the performance rating by experts, fuzzy systems are applied to

model the coefficients of variables in the objective function. Three criteria, level of

roughness (low, medium, high), level of cracking (low, medium, high) and index to

first crack (1, 2, 3, 4 & 5) based on the time taken for the first crack to develop after

construction) were considered for defining the performance rating and condition states

of pavements. Cost matrices, transition probability matrices and pavement condition

data include the input data for the NOS model. In order to numerically generate the

objective function 'W1ik', (proportion of roads of a given category that is in a condition

state' i' at the beginning of 1st time period of planning horizon 'T' to which a preserving

action 'k' is applied), a utility value represented by the performance rating 'fi' which

numerically represents the contribution of a condition state to the overall pavement

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performance is used. Performance rating 'fi' is calculated as fi = Pr*Ir+Pc*Ic+Pi*Ii,

where Pr, Pc and Pi are the performance rating for roughness, cracking and index to

first crack. Ir, Ic and Ii are their importance weights based on their contribution to

overall pavement condition. The performance rating for each pavement condition is

arrived in two steps: i) computation of fuzzy membership function for each condition

state ii) arriving at the final value of performance rating for each condition state which

are used as the coefficients in the objective function of NOS model. Authors arrived at

a conclusion that the annual budget requirement is most sensitive to the requirement of

low roughness. It was also observed that rational range of proportion of pavements in

low roughness and low cracking is about 90% and any need for a higher proportion will

result in an exponential increase in budget requirement.

Ferriera et al. (2002 a) developed a segment linked optimisation model in a

deterministic pavement management system. The objective of the model is to arrive at

the least discounted cost M&R strategy for various segments in a road network.

Pavement condition is expressed with respect to cracking, surface disintegration,

rutting and longitudinal roughness (IRI) and overall quality of pavement expressed in

terms of Present Serviceability Index (PSI). The evolution of pavement condition over

time was predicted using deterministic performance models and a genetic algorithm

called GENETIPAV-D was developed to solve the optimisation problem. Ferriera et

al. (2002 b) also developed a probabilistic segment linked optimisation model together

with a genetic algorithm heuristic with the objective of minimising the total discounted

cost of M&R actions.

Reddy et al. (2002) conducted a study on the performance based cost

allocation of flexible pavement maintenance and strengthening strategies. Pavement

performance models were developed using the data collected for Pavement

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Deterioration Models (POM) research projects sponsored by the Government of India

and the applications of those models to flexible pavement maintenance and

strengthening were studied. Statistical model for unevenness progression and its

relationship with Present Serviceability Rating (PSR) was developed and performance

based overlay thickness charts were developed. The cost allocation for optimum

maintenance and strengthening strategy was determined considering the yearly increase

in VOC due to the cumulative traffic loading during the design/analysis period.

Abaza et al. (2005) proposed an optimum stochastic model for pavement

management. This model deploys a non-homogeneous discrete Markov chain for

predicting the future pavement conditions for a given pavement system. A non­

homogeneous transition matrix was constructed to incorporate both the pavement

deterioration and improvement rates. The pavement management problem was

formulated as constrained integer linear optimisation model subjected to budget and

improvement requirement constraints. A decision policy was formulated based on

either maximising the expected pavement condition rating subjected to budget

constraints or minimising the maintenance and rehabilitation costs subjected to

specified pavement condition ratings for a given analysis period. The resulting

optimum model was associated with a non-linearity order that was equal to the number

of time intervals within the analysis period. Instead of solving a single nonlinear

problem which was very complex, a series of linear problems were formulated and

iteratively solved so that optimal solution for one problem was the input to next one.

The sample results showed that the model was effective in yielding optimum pavement

condition.

Herabat et al. (2005) developed a multi-objective optimisation model to

support the multi-year decision making process of the highway maintenance

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management in Thailand. Both single and multi-objective optimisation models were

developed for a multi-year maintenance planning by incorporating constraint based

Genetic Algorithms to deal with combinatorial characteristics of the network level

maintenance planning. Single objective model considers vehicle operating cost

minimisation and multi-objective model considers maximisation of road network

condition (in terms of roughness) also subject to constraints of budget minimisation and

system preservation. Integer coding (0 to 5) was used for six types of preventive

maintenance treatments including Do Nothing to Rehabilitation with Asphaltic

Concrete. Developed models were validated by comparing with optimal planning from

Thailand Pavement Management System (TPMS).

Priya (2008) developed a comprehensive framework for the optimisation of

the project level pavement management system which integrates both preventive

maintenance and rehabilitation actions with due consideration to the uncertainties in the

pavement performance prediction and maintenance history during design life. Both

deterministic and stochastic optimisation models were developed with the objective of

maximising discounted benefits due to M&R actions during the analysis period. The

deterministic model was formulated as a mixed integer non-linear programming

problem and was solved by network optimisation technique and the stochastic model

was formulated as a dynamic programming problem and solved using. backward

induction. A sensitivity analysis of the design parameters on the optimal pavement

maintenance option was also done. The model developed can be used to quantify the

benefits due to preventive maintenance under various budget levels and to evaluate the

optimum and minimum budget level required to keep the pavement at a specific

performance level. The proposed models offer the framework for more powerful and

rigorous decision support tools.

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Kuhn (2010) used approximate dynamic programmmg to manage a large

network of related sections of pavement, each one of which may be plagued by a

number of different distresses. Approximate dynamic programming mitigates the curse

of dimensionality that has haunted distinct Markov decision problem formulations of

the infrastructure management problem and thus limited their complexity.

A computational study was also done to illustrate how the proposed approach leads to

more sophisticated maintenance decision rules, which can be used to ensure that

suggestions of Pavement Management Systems match more closely with the best

engineering practices.

Jorge and Ferriera (2011) developed a new Maintenance Optimisation System

(MOS), called GENEPAV-HDM4, which was developed to integrate the Pavement

Management System (PMS) of the Municipality of Viseu (Portugal). Currently, the

MOS of this PMS uses a global deterministic pavement performance prediction model

which makes part of the AASHTO flexible pavement design method. The new MOS

(GENEPAV-HDM4) uses a similar optimisation model, but the AASHTO pavement

performance prediction model was substituted by the Highway Development and

Management (HDM-4) pavement performance prediction models to take into account

recent Portuguese legislation. The results obtained by the application of the new MOS

to the main road network of Viseu clearly indicate that it is a valuable addition to the

road engineer's toolbox.

Garza et al. (2011) developed a simpler, yet useful network level pavement

maintenance optimisation model, which is a Linear Program (LP) subject to budget

constraints and the agencies' pavement performance goals in terms of total lane-miles

in each pavement condition state. A decision-making tool was developed using

'Frontline Systems Risk Solver Platform' add-in for Microsoft Office Excel.

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This decision-making tool was able to compute the optimal amount of investment for

each pavement treatment type in a given funding period. The results presented

explained how an annual highway maintenance budget needs to be allocated or

determined to achieve the District's value proposition for various scenarios.

Veeraragavan and Murali (2011) developed decision support models for asset

management of low-volume roads. Pavement performance models developed included

prediction of deflection, roughness, rutting and cracking. An analysis was also done to

quantify the extra funds needed for maintenance as a result of delayed maintenance. It

was observed that a delay in maintenance by 4 years will result in an additional

maintenance cost of US $ 7,791/km to the agency and further delay in maintenance cost

by US $ 9,739/km. The savings in Vehicle Operating Cost for maintenance

interventions at varying IRI values when compared with do-nothing strategy was also

estimated.

2.5 DISCUSSION

Many researchers have experimented the development of performance models

for pavements. Pavement deterioration models developed earlier include both

deterministic and probabilistic models but mainly for major roads. Deterioration

models developed for major roads cannot be not applied for low volume rural roads

since the construction and maintenance practices and traffic characteristics for rural

roads (low volume roads) are entirely different. Consequently the deterioration

mechanism for rural roads also differs from that of major roads. Though few

deterioration models have been developed for rural roads, they follow the same model

form as that of major roads. The contributory factors for deterioration of rural roads

may not be traffic and the axle loads carried, as that for major roads since they are of

low value for rural roads. Further, in Pavement Performance Study (Sood et al., 1996)

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an attempt was made to include the quality of construction of roads as a parameter in

the deterioration model, but it was assigned a value of zero and one for good

construction quality and bad construction quality respectively without proper

quantification. Provision of proper drainage also is an important parameter in the

performance of rural roads which also was not accounted properly in the deterioration

models developed earlier.

Pavement condition prediction model in terms of a composite condition index

like PSR or PCI will represent the overall condition of the pavement and hence more

suitable for development of an optimal maintenance strategy. The PCI prediction

model developed by Thube et al. (2007) for rural roads of India accounts age only as a

parameter which sounds unreasonable. Reddy et al. (1997) developed a prediction

model for prediction of PSR for National Highways and State Highways which cannot

be used for application on rural roads. Hence a thorough investigation is needed

regarding the performance of rural roads over years in order to model its deterioration

mechanism realistically. There is also an immediate need to develop performance

models reflecting the real deterioration process of rural road so as to develop a proper

PMMS for the proper up keep of rural road network which plays a vital role in Indian

Economy.

Prioritisation of rural roads for carrymg out maintenance action is being

exercised at present based on the Pavement Condition Index (PCI) of roads. When

there is a restraint regarding the availability of resources, the optimisation of

maintenance strategies may sound impractical and prioritisation of road sections for

maintenance action may be more reliable. But the prioritisation process should not be

too subjective, and should be based on the condition of pavement which in turn

depends on all types of distresses on the pavement and its roughness. Many researchers

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have made use of the advantage of Fuzzy Multi Criteria Decision Making approach for

prioritisation in various engineering applications (Chen and Klein, 1997; Chen, c.T.,

2001; Chen, M. F., et aI., 2003).

Though HDM-4 is an effective tool used in the Pavement Maintenance and

Management System but it should be properly calibrated to the regional conditions

before application. Calibration of Deterioration and Work Effect models in HDM-4 for

rural roads also should be done considering prevailing traffic conditions and actual

deterioration mechanism. Existing Calibration factors developed for rural roads (Jain

et al. 2007) seem to be not matching with the traffic characteristics and construction

practices of rural roads. Rural roads are generally constructed with Pre-mix Carpet

surface course which is an open graded course. Distresses like ravelling and pot holes

which are categorised as surface disintegration are expected to progress at a faster rate

for open graded surface course of rural roads. So also it is expected that the rate of

progression of load associated distresses like cracking and rutting will be at a slow rate

for rural roads. But the calibration factors for rural roads available in literature are

contradictory to this expectation and hence it is mandatory that HDM-4 should be

properly calibrated incorporating the actual rural conditions before application.

Though optimised maintenance strategy using HDM-4 has been developed by many

researchers, the inadequacy of calibration process for rural roads will be reflected in

that exercise.

The preservation of flexible pavements is possible in many ways and

numerous M&R strategies are possible satisfying both engmeermg and budget

constraints. Hence selection of the optimal maintenance strategy should be done after

considering all feasible maintenance strategies. Extensive research works have been

done in the area of development of optimised maintenance decision support system

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using various techniques (as cited in Section. 2.4.3) and most of them are for major

roads. Various optimisation techniques including application of soft computing

techniques, linear programming, mixed integer nonlinear programming and network

optimisation technique have been adopted by many researchers. Many objectives have

been selected for the optimisation models developed so far including minimisation of

maintenance cost or vehicle operating cost or maximisation of pavement performance

or benefits due to maintenance activity. However, an optimal maintenance strategy

addressing the constraints and limitations prevailing on rural roads has yet not been

developed.

Hence there is a need to develop a comprehensive Pavement Maintenance and

Management System incorporating the pavement deterioration models applicable

specifically for rural roads. This research work is an effort towards the development of

an optimised maintenance strategy incorporating objectives of both minimisation of

maintenance cost and maximisation of performance and hence can be a solution for the

inadequacies confronted by the maintenance sector of rural roads.

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