pavement deterioration modeling in india

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Pavement Deterioration Modeling in India

V. K. Sood, B. M Sharma,

1 .

K. Kanch an, and K. Sitaramanjaneyulu,

Central Road Research Institute India

The large and ever-increasing investment demands for the

upkeep a nd for en suring the desired level of serviceability of

roa d infrastruc ture facilities that w ere created at great cost have

concerned administrators, policy makers, and highway pro-

fessionals in India, and caused them to seek appro priate

solutions, in view of resource constraints, for road maintenance

and reh abilitation prob lems. The development of a pave-

ment management system f or d ifferent conditions prevailing

in the country is a step in this direction.

A

number of studies

have been com pleted for achieving this objective, and a long-

range project entitled the Pavement Perform ance Study (PPS)

is in progress; its goal is to develop data for a total trans-

portation cost model for Indian conditions. The part o f the

PPS

project on Existing Pavement Sections was completed

recently, and pavement deterioration models have been de-

veloped. Separate models are available for estimation of dif-

feren t modes of distress for different types of surfaces. The study

plans and the models developed under the study are pre-

sented, their limitations are described, and future w ork plans

are discussed. The influence of pavement structure, traffic,

and environm ental factors on the progression of cracks and

roughness is illustrated.

A

efficient and adequate transportation system is

one of the key indicators of a nation's prosperity,

its developmental status, and overall economic

growth. India, being the second most populous and the

tenth-largest industrialized country in the world, has an

extensive road transportation system. The roads pass

through areas with extreme climatic conditions-from

heavy rainfall to desert conditions; diverse terrains-

from plains to extremely high m ountain peaks; a nd vary-

ing soil subgrades-rocky and gravelly to marshy land.

Over the p ast four decades, the share

of

total rail and road

traffic carrying passengers and goods has gradually in-

creased from about

24

percent and

11

percent, respec-

tively, in 19 51 to abo ut 8 0 percent and 58 percent,

respectively, in 1990. Road length has increased corre-

spondingly, from 0.4 million km in 1 95 1 o

2

million km,

giving a ro ad den sity of 5 9 kmilOO km'. Because of fast

and ever-increasing industrial, commercial, and other

socioeconomic development activities, the road trans port

vehicle ~ op ul at io n, articularly vehicles carrying goods,

has also increased phenom enally durin g this period.

Efforts are under way in India to develop rational

pavement design procedures that are based on mechanis-

tic principles (critical strain criteria) to replace current

pavem ent design methods, such as the California bearing

ratio (CBR), which are based on an empirical app roach.

Con struction in stages is currently in vogue because of the

paucity of resources. M anu al construction m ethods, used

for many years, are gradually being replaced by mecha-

nized methods, especially for the arterial road network

and high-density corridors. The engineer's judgment and

experience are relied up on heavily in decision making for

maintenance an d rehabilitation

(M R)

of the road netw ork.

Ad hoc M R nor ms are established for assessing mainte-

3rd International Conference on Managing Pavements (1994)

TRB Committee AFD10 on Pavement Management Systems is providing the information contained herein for use by individual practitioners in state and local

transportation

agencies,

researchers

in

academic

institutions,

and

other

members

of

the

transportation

research

community.

The

information

in

this

paper

was

taken

directly

from

the

submission

of

the

author(s).



48 T H I R D I N T E R N A T I O N A L C O N F E R 1

N C E O N M A N A G I N G P A V E ME N TS

nance budgets; such norms are not hased on scientific or

systematic studies undertaken for the purpose.

Because of economies in road transportation, over-

loading by truck operators is common. The majority of

the arterial roa d system experiences overload ing, as muc h

as 38- to 20-ton ne axle loads versus the permissible legal

limit of 10.2 tonnes. The existing road network has

shown signs of premature distress because of the unex-

pected demands of growing traffic volume and heavier

axle loads. The network has fallen short of its structural

capacity a nd hence it is greatly overstrained . The fun ds al-

located for road development programs have been de-

creasing constantly over the years as a percentage of the

gross national product (GN P). The majority of allocated

funds are utilized for providing M&R measures to the

existing network rather than for new construction. The

funds being provided for the arterial road network are on

the order of 50 to 6 0 percent of the amou nt needed.

On the basis of recent studies, it has been determined

that

3

km of every

5

km

1)

of the arterial road network

(national highways and state highways) is below required

standards and needs upgrading. Th e studies also indicate

th at the country is losing abo ut Rs 60 billion (U.S. 2 hil-

lion) per year i11 additional vehicle operation costs be-

cause of poo r road condition s.

A

Ro ad User Cost Study in

India completed in the early 1 980 s and u pdated recently

has successfully brought out the road user cost models

(vehicle operating costs, accident costs, travel tim e costs,

etc.) under varying traffic, roadway, and climatic condi-

tions. It has been estimated tha t abo ut Rs 60 0 billion (U.S.

20 billion) would be needed up to the year 20 00 for im -

proving and upgrading the national highways (2).

The concept of total transportation cosdlife-cycle cost

and the ap plication of pavement managem ent techniques

have been recognized in India recently as versatile tools

for tackling road maintenance and rehabilitation prob-

lems to achieve efficient and effective utilization of mea -

gre available resources. Some studies have already been

completed an d others are in progress. The results are be-

ing used for developing a suitable pavement maintenance

managem ent system for Indian conditions

3).

Pavement performance data are required fo r the devel-

opm ent of appropriate pavement deterioration models. A

num ber of studies have been conducted to achieve this ob-

jective. Most of the studies, such as the AASHO Road

Test, and the Kenya and Brazil studies, were completed

for local conditions. The World Bank model, HDM-I11

4),

was developed on the basis of data from studies on

Kenya, Brazil, a nd the Caribbean. It is currently finding

global app lication after being calibrated for local condition s.

Some studies were also conducted in India recently. Dur

ing the mid-1980s, the Central Road Research Institut

completed a short-term study on development of riding

quality models for purposes of maintenance accounta

bility 5).But the analysis was hased on two series o

observations and because there was no in-depth charac

terization of materials in the laboratory, the prediction

were useful only as a rough tool f or planning . Th e Universi

of Roorkee developed models for predicting th e life of a

overlay ( 6) .These models are hased on performance dat

from overlaid flexible pavements for the period 19 80 t

1990. The models indicate that there is an exponentia

variation of characteristic deflection, rut depth, crac

length, and maintenance cost with time. In addition t o th

models for individu al distress modes a nd soil types, a gen

eral model was also developed for considering the data fo

all of th e test sections; it predic ts the life of ov erlays of di

ferent materials and thicknesses.

Because of a lack of adequate data to generate com

prehensive deterioration models and to cover

a

variety o

parameters, a long-term study sponsored by the India

Ministry of Surface Transpo rt was initiated in 1 985 . Th

details of the study and the models developed are de

scribed in this paper.

Broad bjectives

The Pavement Performance Study, a sequel t o the already

completed R oad User Cost Study, wa s undertake n for th

primary purpose of developing data for a total trans

portation cost model through the following:

1.

Development of pavement performance data fo

pavement m aterials normally used in the country;

2. On the basis of performance data, development o

layer equivalencies, as feasible;

3

Con duc t of limited stud ies of the effect of the m ain

tenance level on pavement performance; a nd

4.

Generation of data on th e construction a nd mainte

nance inputs of different pavements.

The study com prised two parts:

1 The study on Existing Pavement Sections (EPS

conducted on in-service road sections for expeditiou

development of approximate pavement deterioratio

models; and

2. The study o n New Pavement Sections (NPS ), to h

conducted o n specially designed and constructed exper

men tal sections on in-service highways. N PS will provid

more accurate data generation, refinement of models de

veloped under the study on Existing Pavement Section



transportation agencies, researchers in academic institutions, and other members of the transportation research community. The information in this paper was

taken

directly

from

the

submission

of

the

author(s).



and detailed coverage of parameters over a period of

about 10 years. This part of study, still in its initial stage,

is scheduled for completion by the year 2000.

Study on Existing Pavement Sections

A total of 113 test sections, each 500 m long, on the

existing highways were selected for collecting periodic

pavement performance data over a period ranging from

3 to 5 years. The parameters included in the study are as

follows:

1.

Pavement state

Original construction

Overlaid 1 5 years

Overlaid >5 years

2. Traffic

Medium: 0.4 million-0.8 million equivalent stan-

dard axles (MESA)llaneiyear [600 to 1,200 com-

mercial vehicles per day (CVPD)]

High: >1.0 MESMlanelyear (>1,500 CVPD)

3. Climate

Drylsemiarid (rainfall 15 00 mmlyear)

Moist/subhumid (rainfall >SO0 mmlyear)

4. Pavement condition

Good (no distress)

Fairlpoor (>lo% distress)

5. Pavement surfacings

Premix carpet with seal coat

Asphalt concrete

Semidense carpet

6. Maintenance

Deferred level

Normal level

Higher than normal level

Field Investigations and Performance Monitoring

Six series of periodic performance observations, at

6-month intervals, were made on 40 sections, and 10

series of observations were made on 73 sections, for a

total of 113 test sections. The periodic observations and

measurements taken included the following:

Roughness (fifth-wheel hump integrator),

Deflection (Benkelman beam),

Pavement surface distress (actual measurements of

various modes of distress),

Suhgrade moisture content,

Traffic volume (72-hr count),

Axle load survey (annually, random sampling for

72 hr on 35 selected locations),

Transverse profile, and

Lateral placement of vehicles ( two times during the

sixth and tenth series of performance observations on

selected locations).

The data collected from the office records and the field

were computerized and analyzed in three categories.

Static Pavement haracteristics

Static data on pavement characteristics included the cate-

gory of road type, pavement thickness, and composition

of different layers, pavement width, shoulder details.

The pavement strength was expressed as a structural

numher (SN). The strength coefficients assumed for dif-

ferent layers and materials are given in Table 1. The struc-

tural number was improved by including the effect of

subgrade strength, as follows, and termed the modified

structural numher (MSN).

MSN SN + 3.51 log (CBR)

0.85 (log C B R ) ~ 1.43

1)

where CBR is the percentage of in situ CBR at field den-

sity and moisture content.

In some cases, because of inadequate sample size, the

CBR at field conditions could not he determined in the

laboratory, so it was estimated from the following corre-

iation developed by subjecting the available data to re-

gression analysis t values are indicated in parentheses):

CBR -14.004

+

0.345 (+2.36 mm )

+

0.141 (SC)

(4.54) (4.05)

+

0.154 (PI)

+

17.247 (FDD)

(2.37) (2.30)

0.345 (FMC) R2 0.73 (2 )

(2.36)

where

CBR

CBR

at field condition,

+2.36 mm fraction retained on 2.36-mm sieve

( ),

SC sand content

( ),

PI

plasticity index,

FDD field dry density (gmlcc),and

FMC field moisture content

( ).

plot of observed and estimated values is given in

Figure

1.




taken

directly

from

the

submission

of

the

author(s).



T H I R D INTERNATIOKAL CONFERENCE

O N

MANAGING PAVEMENTS

TABLE

Strength Coefficients

-

Layer Coeff~cient

z

a

3 0

Surfacing

o

0.210.18

sphaltic concrete AC) 40mml25mm

Semi-dense carpet SDC) 25mm 0.16

-

Premix carpet PC) 20mm 0.12

Base Course

0.14

ituminous macadam BM)

m

Built-up spray grout BUSG) 0.13

Thin bituminous layer BT) 0.12

Water bound macadam WBM) 0.12

Subbase

Water bound macadam oversized)

WBM-OS) 0.12

Brick soling 0.10 0 5 1 0 1 5

2 0 2 5 3 0 1

Brick ballast 0.10 O b s e r v e d

C B R (

U n s o a k e d

)

Kankar

0.08

FIGURE

Plot between estimated and observed CUR values

Dynamic Pavement Condition

mercial traffic) and axle loads. The damaging effect of v

hicles was expressed by the vehicle damage factor VDF

Dynamic data on pavement condition included periodic

Equivalent standard axles were calculated for each seri

pavement performance data such as roughness mm/km),

from the traffic data and the VDFs. The cumulative stan

characteristic deflection mm) , subgrade moisture con-

dard axles were derived for each series of observation pe

tent ( ), and different forms of pavement distress

riods. A typical

lot

of the VDFs and equivalen

expressed as percentage of area with respect to total pave-

single-axle loads ESALs) as observed on some of the ex

ment surface area.

perimental sites at different periods during the course o

study is given in Figure 2

Traffic Characterization

Data Sorting and Smoothing

Traffic characterization data included the details of traffic

volume time of day, direction, and average daily corn

As is expected from a study of this nature and magnitud

N OT ES -' . T H E F I G U R ES W I T H I N B R A C K E T S

I N D I C A T E E S A L S I O A Y

2 S E R I E S 1 ,2 ,3 R E P R E S E N T T H E

P E R I O D I C S U R V E Y S

S i t e

1

S i t e

2

S i t e 3 S i t e

L

FIGURE

Variation in VDF and ESALS per day on some experimental sites during the

study period.



transportation

agencies,

researchers

in

academic

institutions,

and

other

members

of

the

transportation

research

community.

The

information

in

this

paper

was

taken

directly

from

the

submission

of

the

author(s).



a large variation in the performance data was observed.

Some of the data trends were not justifiable. In order to

overcome the large data variation, particularly that in

roughness because of random and systematic errors, the ex-

ercise of sorting and smoothing data was undertaken.

Data that were unconvincing and extremely out of the

population were excluded from the analysis. The plots of

roughness for each section for different series of measure-

ments were examined individually in the light of mainte-

nance input data provided by the field engineers. In the

absence of any supporting data for the improvement in

roughness, the roughness being a monotonically increas-

ing function with time and axle loadings if resurfacing

or rehabilitation is not done), such data were removed,

and the whole observation period was divided into

subperiods.

The loss of excluded data was mitigated by processing

the time-series data to determine the section-specific rate

of roughness progression over time by a log linear regres-

sion of roughness against time. This was done to minimize

the impact of random errors in roughness. The roughness

was estimated by using the following equation for model

development:

log g

a,

a,PAGE

3 )

where

g

is the observed roughness mm km ) and PAGE

is pavement age months).

Model Development

The large amount of data collected was subjected to the

following forms of analysis: graphical, linear or nonlinear

regression, and multivariate linear or nonlinear analysis.

Data reviews were undertaken from time to time, and

trends between different independent parameters, namely,

MSN versus deflection, roughness versus cumulative stan-

dard axles CSALs),distress versus CSAL, pavement age

versus roughness, and so forth, were plotted to examine

the behavior and interactions of different parameters. The

incremental approach, taking the difference between the

two successive observations, was adopted in multivariate

regression analysis as the most logical approach available

for time-series data analysis for the predictions of change

over the preceding value.

The range of parameters covered in the data analysis

for model development is given in Table 2. As seen from

the table, the wide variation in pavement conditions nor-

mally observed in India was covered in the study. The val-

ues in Table

2

are the changes in different parameters

between the two sets of observations included in the data

analysis after the sorting and smoothing of the data.

These changes are over a period ranging from 0 417 to

1 917 years for premix carpet and from 0 333 to 1 333

years for asphalt concrete. The negative changes in some

of the parameters are due to improvements in surface con-

dition resulting from maintenance inputs made from time

to rime during the study period.

Different forms of statistical models were developed

for prediction of pavement deterioration from the data

obtained after the sorting and smoothing techniques. The

general form of models for the prediction of change in

roughness and cracking are as follows:

Change in roughness ficurrent roughness, change

in surface condition, traffic

volume and loading, pave-

ment age, pavement strength,

maintenance inputs)

Change in distress flcurrent surface condition,

traffic volume and loading,

pavement age, pavement

strength, maintenance inputs)

Separate models developed for roughness progression

and crack progression and for asphalt concrete and pre-

mix carpet surfaces are given in Table 3 These models

permit the prediction of change in roughness and crack-

ing over time. Plots of observed and estimated values for

roughness and crack progression for a premix carpet sur-

face are given in Figures 3 and 4

Separate models were developed for two different types of

pavement surfaces normally used in India. The models

were partly validated also with the data available from

some of the experimental sections. It was found that pre-

dictions for pavement deterioration, especially in terms

of roughness, can be made with reasonable accuracy.

Though the experiment provided for the study of the ef-

fect of different levels of maintenance on pavement per-

formance, the same could not be quantified because of a

lack of feedback on maintenance inputs. For the purpose

of these models, it was assumed that the test sections were

provided with routine maintenance only.

The models presented in this paper are based on the typi-

cal traffic, environmental, and pavement conditions on

the malor highways in India. These models will provide use-

ful input for the development of an appropriate pavement

management system for maintenance planning and for

setting priorities. There is still much data available for

other forms of analysis. In view of the limitations of

these models, further work on different aspects of model-

ing is being continued. The inclusion of deflection in the

models as an indicator of pavement strength is under ex-




taken

directly

from

the

submission

of

the

author(s).



T BLE Range o Study Parameters

S1. Parameter Range

No. Premix Carpet Asohaltic Concrete

Minimum Maximum Minimum Maximum

1 APHt

0 . 8 3 0 1 . 5 0 0 0 . 7 7 7 1 . 4 0 0

2 ACRt

3 7 . 6 9 0

5 2 . 9 0 0 2 . 5 5 0 1 8 . 7 6 0

3 APWt -

6 . 9 1 0

1 4 . 4 6 0

8 . 6 1 0 5 . 3 5 0

4

ADEPt

3 . 9 0 0 2 . 6 8 0 1 . 0 0 0 2 . 3 8 0

5

Rat

1 5 1 7 8 6

4 3 3 9 9 5

6

ACSAL

0 . 0 0 1 1 1 . 0 8 0

1 . 0 2 9 1 2 . 9 1 0

7 MSN

1 .

ZOO 4 . 4 8 0

3 . 2 5 0 5 . 0 2 4

8

PAGE

0 . 0 8 3

1 1 . 6 7 0 0 . 8 3 3

1 1 . 6 6 7

9 t

0 . 4 1 7 1 . 9 1 7

0 . 3 3 3 1 . 3 3 3

1 0

Rgi

1 3 3 8

6 6 5 1 1 3 3 4

2 8 6 6

11 CRi

0 . 0 0 0

9 0 . 0 0 0

0 . 0 0 0 2 7 . 9 0 0

where

pxt

= change in potholes 2) over a time t Years)

ACRt

=

Change in Cracking

( )

over a time t years)

APWt

= Change in patch work 2 ) over a time t years)

ADEPt = Change in depression 2 ) over a time t years)

ARgt = Change in roughness mm/km) over a time period

t years)

Rei = Initial rouphnasa mm/km)

CR = Initial cracking ( )

PAGE = Pavement age since last renewal/strengthening

years)

t

=

Time interval years)

MSN = Modified structural number

ACSAL change in cumulative standard axles msa)




taken

directly

from

the

submission

of

the

author(s).



5

4 T H I R D I N T E R N A T I O N A L C O N F E R E N C E O N M A X A G I N G P A V E ME N T S

5 0

C R t

3 2 8 5 6

b C S A L M S ~

m

C6li . t

R~ = 3 5

-

LO

-

-

rn

C

.

.x

2

3 0

C

.

0

0

.D

I

.

FOR PRE MIX CARPET )

.

0 10 2 0 30 LO 50

bserved change in cracki ng 1.)

FIGURE 4 Plot between estimated and observed change in cracking.

plorat ion. Analysis is also planned for predict ion of de-

teriorat ion in terms of combined dis t ress by assigning

sui table weight ing factors to different mode s of dis t ress .

Mo re ref ined model s are expected to be avai l ab le f rom the

s tudy on Ne w Pavement Sections which has a l ready been

launched.

ACKNOWLEDGMENTS

This pa per i s publ ished wi th the permiss ion of th e Di rec-

to r o f the Cent ra l Road R esearch Ins ti tu te . Th e au th ors

gra tefu l ly acknowledge the gu idance prov ided by Y. R .

Phull Depu ty Director an d the assis tance rende red by

Devesh Tiwari scientis t bo th of the Inst i tute. Th an ks are

a l so ex tended to o ther s t aff mem bers o f the Ins t i tu te w ho

ma de con tribut ions dur ing different s tages of the project .

REFERENCES

1 Gupta D. P. M.

K.

Bhalla and S. S. Cha krabo rty. Strategy

for Road and Road Transport Development: Emerging

Issues. lou rn al of the Indian Roads C ongress Vol. 51- 3

Nov. 1990 pp. 597-640.

2

Sikka R. P. Growing Crisis of Managing the Trunk Route

System in India. Journal of the Indian Roads Congress

Vol. 53-2 Sept. 1992 pp. 331-358.

3. Sood V.

K.

B. M. Sharma and K. Sitaramanjaneyulu

Pavement Performa nce Study-An Input Toward s Devel

opment of Pavement Management System in India. Proc.

7th REAAA Conference Singapore V ol. 2 June 199 2

pp. 811-819.

4. Paterson W.D.O. R oad Deterioration a nd Maintenance

Effects. World Bank Publication. The Johns Hopkins Uni-

versity Press Ba ltimore and London 1987 .

5 . Report on Development of Ma i~tcm ance

ased

Preliminmy

Pavement Riding Quality Model for

Tmnk

Routes. Centra

Road Research Institute New Delhi India Ju ne 1986.

6

Jain S. S.

A.

K.

Gup ta and Sanjeev Rastogi. Study of Sn

fluencing Param eters for Efficient Maintenan ce M anage-

ment of Flexible Pavements. Journal of the Indian Roads

Congress Vol. 53-1 June 1992 pp. 93-143.

7 Interim Report on Study on E xisting Pavemeltt Sections

Ce ntral Road Research Institute New Delhi Ind ia D ec

1990.

8. Preliminary Report o n Model Development. Study on Ex

isting Pauement Sections. Central Road Research Institute

New Delhi India March 1993.




taken

directly

from

the

submission

of

the

author(s).

pavement deterioration modeling in india

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