Performance Evaluation of Rigid Pavements

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International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056
Volume: 04 Issue: 03 | Mar-2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1915
Performance Evaluation of Rigid Pavements
Akshay. J1, Inchara.K.P2, Mithun. K3
1Assistant Professor, Dept. of Civil Engineering, KIT, Karnataka, India
2Assistant Professor, Dept. of Civil Engineering, KIT, Karnataka, India
3Assistant Professor, Dept. of Civil Engineering, KIT, Karnataka, India
---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Rural roads play an important role in the
development of the country, as major part of the Indian
population stays in rural areas. The construction of cement
concrete rural roads has gained momentum due to the
central government sponsored program called Pradhana
Mantri Gram Sadak Yojana (PMGSY). In the present study,
nine cement concrete test stretches constructed under
PMGSY connecting many villages in Hangal Taluk, Haveri
District at Karnataka state in India were selected, to develop
the present serviceability index (PSI) equation for cement
concrete roads. To carry out the studies, the test stretches
were selected with varying pavement conditionsfromworst
to good condition. The unevenness of the pavements was
determined using MERLIN, and the unevenness values were
expressed in terms of BI and are measured in millimeters
per kilometer. The distress such as scaling (ravelling),
spalling, faulting, cracking, rut depth etc weremeasured and
recorded. The rating studies were also carried out by
constituting two panels with five raters in each panel. The
ratings of the two panels from the field observations were
subjected to error elimination viz leniency error and central
tendency error. The true ratings were obtained after
application of corrections to the individual ratings. The
distress data and the mean of the corrected ratings of both
visual and ride ratings were used and regression analysis
were carried out to develop PSI equations for these village
roads based on both visual and ride ratings. The pavement
distress data were substituted in the developed models to
obtain the PSI values. Then the values were compared with
the corrected PSR values obtained from rating studies. The
pavement condition index (PCI) values were computed by
the deduct value method.
Key Words: Present Serviceability Index (PSI), Pavement
Condition Index (PCI), International Roughness Index (IRI),
Present Serviceability Rating (PSR)
1. INTRODUCTION
Road transport is an important mode of transport
having many desirable characteristics such as flexibility,
door to door service and accessibility to remote areas. The
development of distress in a road leading to failure can be
considered as a continuation of the development of the
irreversible strain in the road, after a period of initial
compaction due to repeated loadingofoneormore elements
of the road above critical values of stress and strain.
The service life of the pavement would be reached
when any one of the structural or functional parameters
reach a minimum acceptable level,atthatpointmaintenance
would be carried out and a new service life would begin.
Pavement condition consists of four main
components; riding comfort, load carrying capacity, safety
and aesthetics. Pavement condition data are collected to
assist in making decision on highway maintenance,
rehabilitation and reconstruction.
For successful maintenance of the pavements it is
essential to know the present condition of the pavement to
withstand the design traffic under prevailing climate and
environmental conditions. Whenpavementsaresubjected to
increased magnitude of wheel loads the pavement
deterioration starts earlier than the anticipated design life.
The PSI models and PCI help to analyze the present
condition and to provide a proper maintenance to the
pavements to increase the service life.
1.1 Location of the Roads selected for studies:
In the present study, nine different rural road
stretches connecting many villages in Hangal Taluk, Haveri
District at Karnataka State in India wereselected.These nine
stretches were further divided into 20 subsections for more
precise studies.
The different technologies were adopted for the
construction of these roads. These roads were constructed
by under the initiation of Central Government programme
‘Pradha Mantri Gram Sadak Yojana’ (PMGSY).
The roads which are considered for this study
comes under PMGSY and constructed as experimental
stretches with varying the construction material and to
evaluate their performance, durability and cost. The
carriageway width of each road was found to be 3.75 m.
Fig 1: Map showing the project Location & projectroads.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056
Volume: 04 Issue: 03 | Mar-2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1916
2. FIELD STUDIES
2.1 Selection of the Test Stretches
The locations of the test stretches are given in Table 1
Table 1: Selected Test Stretches for Rating Studies
Name of the Road
Technology
Adopted
Age of
pavement
in months
Gopapura (Ch 0.0 - 2.0km) CC Pavement 27
Gopapura (Ch 2.0 - 4.5km) CC Pavement 27
Dommanala (Ch 0.0 - 3.0km) CC Pavement 27
Dommanala (Ch 3.0 - 6.3km) CC Pavement 27
Shringeri (Ch 0.0 - 3.0km) CC Pavement 24
Shringeri (Ch 3.0 – 5.9km) CC Pavement 24
Multhalli (Ch 0.0 – 2.5km) RCCP 31
Multhalli (Ch 2.5 – 5.0km) RCCP 31
Multhalli (Ch 5.0 – 7.9km) RCCP 31
Yaliwala (Ch 0.0 – 3.6km) RCCP 25
Avalagerikoppa(Ch0- 2.5km) RCCP 32
Avalagerikoppa(Ch2.5- 5km) RCCP 32
Avalagerikoppa(Ch5- 7.0km) RCCP 32
Kondoji (Ch 0.0 – 2.0km)
RCCP with
Flyash 27
Kondoji (Ch 2.0 – 3.8km)
RCCP with
Flyash 27
Suraleshwara(Ch 0 - 2.0km)
RCCP with
Flyash 32
Suraleshwara(Ch 2 - 4.2km)
RCCP with
Flyash 32
Basapur (Ch 0.0 – 2.5km)
RCCP with
Flyash 27
Basapur (Ch 2.5 – 5.0km)
RCCP with
Flyash 27
Basapur (Ch 5.0 – 7.9km)
RCCP with
Flyash 27
2.2 Pavement Condition Measurement
 Pavement Unevenness - Pavement unevenness that
affects the vehicle operating cost was measured using a
MERLIN. The Merlin is a simple roughness measuring
machine that has been designed for use in developing
countries. “Merlin stands for – a Machine for Evaluating
Roughness using Low-cost Instrumentation” (Cundill,
1991). Roughness in terms of the Merlin scale, D, is
obtained by first plotting the recorded data onto a
histogram. Five percent of the total number of recorded
observations is counted in from each end of the
distribution with the position marked. The value D, in
millimetres, is then obtained by measuring the distance
between the two marked points, representing a data
spread of 90% for the collected data.
Cundill (1991) a data spread of 90% produced the
highest coefficient of determination (R2), when linear
regression was undertaken using D values derivedfrom
different data percentages. Readers are referred to
Cundill, (1991) for a more detailed description on how
to obtain D, roughness in terms of the Merlin scale.With
the value of D obtained, IRI by correlation can be
calculated from Equation given below.
IRI = 0.593 + 0.047D
Where IRI is the roughness in terms of the
International Roughness Index, measured inmetresper
kilometre (m/km), and D is the roughness in terms of
the Merlin scale, measured in mm.
 Scaling - The scaling surface is identified by the worn
out or the loss of material on the top of the surface. The
scaled surface is measured by converting the scaled
surface into suitable geometric area for measurement
using tape. The scaled surface is expressed in sq meters.
 Faulting Measurement - A three meter straight edge
was used to determine the difference in height between
the two slabs. The straight edge was placed on the
longitudinal direction and faulting depth was measured
with the aid of wedge scale.
 Cracked Area - Crack is defect in the pavement surface,
which weakens the pavement structure. It allows water
to percolate into the pavements which causes further
damage. These cracks were measured in terms of its
spread over area.
Table 2: Roughness and Pavement Distress Data of
Selected Pavement Test Stretches
Stretch
No
IRI
m/km
Cracking
in sq m
Scaling
in sq m
Faulting
in mm
1 3.62 0.15 12.56 2.2
2 3.52 2.54 82.24 4.1
3 8.22 1.87 10.16 7.5
4 6.53 1.8 156.33 14.2
5 7.61 6.9 8.26 3.5
6 6.81 5.8 29.81 6.2
7 8.75 2.2 137.65 11.2
8 8.26 2.4 188.84 10.8
9 8.04 2 166.82 18.2
10 7.61 2.12 97.87 12.8
11 6.89 1.3 127.63 8.6
12 6.34 0.72 116.65 9.2
13 6.15 1.3 118.21 14.8
14 6.69 1.67 106.42 7.4
15 6.37 0.6 142.48 6.8
16 6.60 6.5 138.71 14.2
17 6.24 1 143.45 10.8
18 6.55 3.68 145.17 24.2
19 6.85 6.4 87.09 12.6
20 6.77 4.7 69.76 18.9
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056
Volume: 04 Issue: 03 | Mar-2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1917
2.3 Constituting of Rating Panel
To carry out the PSR studies, three panels were
constituted viz highway, mixed and non highway panels,
with five raters in each panel.
2.4 Visual Rating and Ride Rating
The members of the rating panel were trained to
walk along the left and right wheel paths on the selected
stretches and the condition of the pavement was assessed
based on the visual judgment of surface characteristics. The
rating scale adopted forthevisual assessmentofpavementis
shown in Table 3. Based on the condition of the stretch the
raters were allowed to rate the stretch on the scaleofzeroto
five.
For the rating by riding technique the raters were
taken in a test vehicle driven along the stretches and are
trained to assess the PSR value according to comfort
condition. The rating scaleadoptedfortheridingassessment
of pavement is shown in Table 4.
Table 3: Description of Visual Rating Scale
Sl.
No
Description Based on Visual Condition of
Pavement Surface
Numerical
Scale
1
Perfectly even surface, without undulations,
cracking, patching or scaling.
4-5
2
Slightly uneven surface with some
undulations, Slight cracking.
3-4
3
Moderately uneven surface, medium cracking,
slight faulting or scaling.
2-3
4
Uneven surface with improperly patched
potholes, medium to heavy crackingandscaling
1-2
5
Uneven surface with different type of
undulation, potholes, heavy cracking
0-1
Table 4: Description of Riding Rating Scale
Sl. No.
Description Based on Riding
Condition of Pavement Surface
Numerical
Scale
1 Without discomfort, perfect smoothness 4-5
2 Little distortion, fairly smooth riding 3-4
3 Medium distortion, fair to uneven riding 2-3
4 Heavy distortion, uncomfortable riding 1-2
5 Intolerable, very discomfortable riding 0-1
The following three panels were constituted for rating the
ride and visual ratings:
 Highway panel- The panel consisting of 5 members
having knowledge about highway technology.
 Non highway panel – The panel consisting of 5
members of non highway background.
 Mixed panel- The panel consisting of both the
members of above two panels.
3. ANALYSIS OF DATA
3.1 Analysis of Present Serviceability Ratings
The compiled data were analyzed to estimate the
mean and standard deviation of rating values. The rating
values were thus determined foreachpavementsectionboth
for ride and visual rating techniques. The same data were
analyzed for the estimation and removal of errorspresent in
the rating. The statistical analysis was carried out for the
compiled data thus the values of mean and standard
deviation of each pavement section and for each rater is
calculated.
3.2 Estimation and Removal of Errors in the Individual
Present Serviceability Ratings
The members rated the selectedstretchesboth by visual
and ride rating techniques as per the scale mentioned in the
above tables.
But the ratings were modified due to the following
errors:
 Leniency error - The leniency error was determined by
computing mean of individual rater and mean of all
single ratings and the leniency error is obtained by
subtracting mean of the individual rater and mean of all
single ratings.
 Central tendency error - The central tendency errors are
determined by calculating the standard deviation of
individual raters and standard deviation of all single
ratings.
3.3 Determination of True Ratings
After the determination of errors in the visual and
ride rating values for all the three panels the true present
serviceability ratings were determined. The true ratings
were obtained by adding corrected leniency error and
central tendency error to the field ratings. The mean of the
three panels and the overall mean value both for visual and
ride rating are given in Table 5 and Table 6.
Table 5: Corrected Values of the Visual Ratings for
Selected Pavement Test Stretches
Stretch
No
Highway Mixed
Non
highway
Average
1 4.2 4.0 4.0 4.06
2 4.0 3.7 3.3 3.70
3 3.2 3.0 3.5 3.23
4 2.3 2.5 2.3 2.36
5 3.6 3.6 3.6 3.59
6 3.4 3.6 3.8 3.62
7 2.7 2.8 3.2 2.92
8 2.9 2.9 3.0 2.94
9 2.1 2.4 3.1 2.49
10 3.4 3.3 3.4 3.37
11 2.9 2.9 3.4 3.07
12 2.9 3.1 3.5 3.16
13 2.8 3.1 3.3 3.05
14 3.1 3.1 3.5 3.22
15 3.1 3.0 3.1 3.05
16 2.8 2.9 3.0 2.90
17 3.0 3.2 3.1 3.08
18 1.8 1.6 2.4 1.92
19 2.2 2.1 3.1 2.45
20 2.6 2.9 3.4 2.92
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056
Volume: 04 Issue: 03 | Mar-2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1918
Table 6: Corrected Values of the Ride Ratings for
selected pavement test stretches
Stretch
No Highway Mixed
Non
highway
Average
1 4.2 4.1 4.0 4.13
2 3.8 3.4 3.9 3.71
3 3.7 3.9 3.3 3.61
4 2.4 3.0 2.4 2.60
5 3.4 3.6 3.6 3.55
6 3.3 3.7 3.5 3.51
7 2.6 2.9 3.0 2.82
8 2.6 3.2 3.2 2.98
9 1.9 2.9 3.1 2.64
10 3.2 3.2 3.5 3.30
11 3.3 3.4 3.5 3.41
12 3.5 3.3 3.8 3.54
13 3.0 3.4 3.2 3.19
14 3.2 3.4 3.2 3.27
15 2.7 3.0 3.4 3.02
16 2.9 3.0 3.1 2.99
17 2.8 2.9 2.7 2.85
18 1.5 1.7 2.5 1.88
19 2.2 2.4 3.2 2.61
20 2.8 2.8 3.5 2.99
3.4 Development of Model
The PSI model was developed along the lines of
AASHO equation using the field measurements such as
faulting, IRI, scaling and cracking and mean PSR values. The
PSI equation was developed for both the visual and ride
rating using analysis tool pak and regression analysis. The
PSI models were developed for each of individual distress
and finally a multiple linear regression analysis was carried
out to develop models. The models are given below
Chart-1: The linear graph representing PSR v/s IRI values.
PSI = 4.11 - 0.152 (IRI)
PSI model with roughness as a distress expressed in IRI
Chart-2: The linear graph representing PSR v/s Faulting
values.
PSI = 3.92 – 0.076 (faulting)
PSI model with faulting as a distress expressed in mm
(average) per 100 m2 area.
Chart-3: The linear graph representing PSR v/s Scaling
values.
PSI = 3.73 - 0.0062 (Scaling)
PSI model with scaling as a distress expressed in square
meter per 100 sq m area.
Chart-4: The linear graph representing PSR v/s cracking
values.
PSI = 3.19 - 0.037 (cracking)
PSI model with cracking as a distress expressed in square
meter per 100 sq m area.
3.4.1 Multiple Linear Regression Model for Visual
Ratings
Model: Yi (PSI) = β0+β1 (IRI)+β2 (Faulting) +β3 (Scaling) + β4
R(Cracking) + εi
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056
Volume: 04 Issue: 03 | Mar-2017 www.irjet.net p-ISSN: 2395-0072
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Assumptions
1) The response variable Y is linearly related to regressor
variables IRI, cracking, faulting, scaling and
2) εi (error) i.e. errors are normal with mean zero
independent common variance σ2
.
Model for Visual Rating of Pavements
PSI = 4.31 – 0.039 (IRI) – 0.055 (faulting) - 0.003
(scaling) – 0.025 (cracking)
PSI = Present serviceability Index for the range 0.00 to 5.00
IRI = International Roughness Index,m/kminthe range3.52
to 8.75 m/km
Faulting = faulting in mm in the range 2.2 to 24.2 mm
(average) per 100 m2 area.
Cracking = Cracking, in the range 0.15 to 6.9 square meter
per 100 sq m area
Scaling = Scaling in the range of 8.26 to 188.84 square meter
per 100 sq m area.
Table 7: Model Summary
Model R R Square
Adjusted R
Square
Std. Error
of the
Estimate
Observations
1 0.893 0.798 0.745 0.252 20
A Predictors: (Constant), IRI, Faulting, Cracking and Scaling
B Dependent Variable: PSR
R2 =0.798 tells us that 79.8% of variation in Y is explained
by the regressor variablesIRI,Cracking,FaultingandScaling.
(Note: anything more than 60% is good)
Table 8: Anova
Model
Sum of
Squares
Df
Mean
Square
F
1
Regression 3.783 4 0.945 14.88
Residual 0.953 15 0.063
Total 4.730 19
Coefficients
Standard
Error
t Stat P-value
Intercept 4.313 0.305 14.12 4.53E-10
IRI -0.039 0.048 -0.81 0.427
Cracking -0.025 0.031 -0.79 0.439
Faulting -0.055 0.013 -4.11 0.0009
Scaling -0.003 0.001 -2.09 0.053
3.4.2 Multiple Linear Regression Model for Ride
Ratings
Model: Yi (PSI)= β0+β1 (IRI) +β2 (Faulting) +β3 (Scaling) +
β4 R (Cracking) + εi
Assumptions
1) The response variable Y is linearly related to
regressor variables IRI, cracking, faulting, scaling
and
2) εi (error ) i.e. errors are normal with mean zero
independent common variance σ2
.
Model for Ride Rating of Pavements
PSI = 4.37 – 0.022 IRI – 0.051(faulting) - 0.003(Scaling)–
0.050(cracking)
PSI = Present serviceability Index for the range 0.00 to
5.00
IRI = International Roughness Index, m/km in the range
3.52 to 8.75 m/km
Faulting = faulting in mm in the range 2.2 to 24.6 mm
(average) per 100 m2 area.
Cracking = Cracking, in the range 0.15 to 6.9 square meter
per 100 sq m area.
Scaling = Scaling in the range of 8.26 to 188.84 square
meter per 100 sq m area.
Table 9: Model Summary
Model R R Square
Adjusted R
Square
Std. Error
of the
Estimate
Observations
1 0.901 0.812 0.762 0.245 20
A Predictors: (Constant), IRI, Faulting, Cracking and
Scaling
B Dependent Variable: PSR
R2 =0.90 tells us that 90% of variation in Y is explained by
the regressor variables IRI, Cracking, Faulting and Scaling.
(Note: anything more than 60% is good)
Table 10: Anova
Model
Sum of
Squares
Df
Mean
Square
F
Sig.
1
Regression 3.898 4 0.974 16.23 2.52E-05
Residual 0.900 15 0.060
Total 4.798 19
Coefficients
Standard
Error
t Stat P-value
Intercept 4.37 0.296 14.75 2.45E-10
IRI -0.022 0.046 -0.485 0.634
Cracking -0.050 0.030 -1.652 0.119
Faulting -0.003 0.013 -3.881 0.001
Scaling -0.051 0.001 -2.704 0.016
3.4.3 Determination of Pavement Condition Index (PCI)
The PCI was calculated according to ASTM Standards.
PCI =PCI max - ∑ Deduct
PCI – individual condition index based on measured
condition.
PCI max = value for perfect condition with no measured
defects =100
Deduct – deduct value assigned to distress type severity and
extent.
Table 11: The PCI Values and Condition of Pavement
for Selected Stretches.
Stretch
No
Cracking
Faulti
ng
Scaling TDV CDV PCI Condition
1 0.15 2.2 12.56 2 2 98 good
2 2.54 4.1 82.24 26 15 85 Satisfactory
3 1.87 7.5 10.16 16 9 91 Good
4 1.8 14.2 156.33 71 46 54 Poor
5 6.9 3.5 8.26 15 8 92 Good
6 5.8 6.2 29.81 24 16 84 Satisfactory
7 2.2 11.2 137.65 63 41 59 Fair
8 2.4 10.8 188.84 74 49 51 Poor
9 2 18.2 166.82 77 51 49 Poor
10 2.12 12.8 97.87 60 38 62 Fair
11 1.3 8.6 127.63 35 23 77 Satisfactory
12 0.72 9.2 116.65 56 37 63 Fair
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056
Volume: 04 Issue: 03 | Mar-2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1920
13 1.3 14.8 118.21 62 41 59 Fair
14 1.67 7.4 106.42 52 33 67 Fair
15 0.6 6.8 142.48 58 36 64 Fair
16 6.5 14.2 138.71 44 27 73 Satisfactory
17 1 10.8 143.45 64 42 58 Fair
18 3.68 24.2 145.17 82 53 47 Poor
19 6.4 12.6 87.09 73 48 52 Poor
20 4.7 18.9 69.76 64 40 60 fair
4. CONCLUSIONS
The following conclusion has been drawn from the study:
 The pavement selected varies from worst to good
condition covering the boundaries for the model. The
PSI models developed for visual and rideratingshows
IRI has a major influence on the PSI model and in ride
rating model faulting has also a major influence
compared to scaling and cracking.
For Visual rating studies,
PSI=4.31–0.039(IRI)–0.055(faulting)-0.003(scaling)
–0.025(cracking)
For Ride rating studies.
PSI=4.37–0.022 IRI–0.051(faulting) -0.003(Scaling)
–0.050(cracking)
 The IRI and age has a great influence on the PSI. PSI
starts decreasing with increasing in age and IRI.
 Based on the remaining service life the lowest service
life is 140 months and highest is 148 months. According
to this the concrete rural roads provide better service
upto 12 years based on roughness.
Using the Pavement Condition Index (PCI) values by
deduct value method 3 stretches were found to be in good
condition, 4 were satisfactory, 8 were in fair condition and
remaining 5 in poor condition. The plain cement concrete
roads have given better performance compared to the other
roads constructed using different materials and different
technologies. Hence the plain cement concrete pavements
are in good condition and the other pavement conditions
vary from satisfactory to poor condition.
REFERENCES
1. Carey, W.N. And Irick, P.E., “The Pavement Serviceability
– Performance Concept”, HRB Bulletin 250, 1960.
2. Hans Prem and Geoff Ayton “Improved Techniques for
Assessing Ride Quality on Concrete Pavements”.
3. Hutchinson, B,G., “Priniciples of Subjective Rating Scale
Construction”, Highway Research Record No 46,
Highway Research Board, Washington D. C., 1964.
4. Chenung M .S and Kyle B R “Service life prediction of
concrete structures by reliability analysis” architectural
and engineering services, public worksandgovernment
services Canada
5. Prozzi,J.A., and Madanat, S.M., “DevelopmentofPavement
Performance Models by Combining Experimental and
Field Data”, Journal of Infrastructure systems,American
Society of Civil Engineers, 9-22, 2004
6. Phull, Y.R., “Evaluation of Pavement Condition by
Serviceability Rating Technique”, Seminar on
Strengthening of Existing Road Pavement, IRC
Publication, 1971.
7. Dossey and Hudson “Performance Prediction Models for
Rigid Pavements” in Texas.
8. Susan Rose, Kuncheria.P.Issac, and Binu Sara Mathew.,
“Development of Deterioration Models for Rural Roads”,
College of Engineering, TVM, India, 280-284, Oct 2007
9. Jorge A. Prozzi and Samer M.Madanat., “Incremental
Nonlinear Model for Predicting Pavement Serviceability”,
Journal ofTransportationEngineering,AmericanSociety
of Civil Engineers, 635-641, 2003
10. ASTM Designation: D6433 – 11 “Standard Practice for
Roads and Parking Lots Pavement Condition Index”
11. Bandara N. and Gunarathne.M., “Current and Future
Pavement Maintenance Prioritization Based on Rapid
Visual Condition Evaluation”, Journal of Transportation
Engineering, American Society of Civil Engineers, 116-
123, 2001.
12. M.A Cundill, “The MERLIN low-cost road roughness
measuring machine”, TransportationandRoadResearch
Laboratory Research Report 301.
13. Dr. L.R. Kadiyali, Dr. N.B. Lal “Principles and Practices of
Highway Engineering”, Khanna Publishers. New Delhi.
14. Indian Roads Congress. “Journal of the Indian Roads
Congress Volume 68-1” Published by the IRC New Delhi
April-June 2007.
15. Greggory Morrow. “Comparison of RoughnessMeasuring
Instruments” Department of Civil and Environmental
Engineering University of Auckland Private Bag 92109
Auckland New Zealand May 2006.

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SUMIT SQL PROJECT SUPERSTORE 1.pptxSUMIT SQL PROJECT SUPERSTORE 1.pptx
SUMIT SQL PROJECT SUPERSTORE 1.pptx
Sumit Jadhav 15 visualizações

Performance Evaluation of Rigid Pavements

  • 1. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 04 Issue: 03 | Mar-2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1915 Performance Evaluation of Rigid Pavements Akshay. J1, Inchara.K.P2, Mithun. K3 1Assistant Professor, Dept. of Civil Engineering, KIT, Karnataka, India 2Assistant Professor, Dept. of Civil Engineering, KIT, Karnataka, India 3Assistant Professor, Dept. of Civil Engineering, KIT, Karnataka, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Rural roads play an important role in the development of the country, as major part of the Indian population stays in rural areas. The construction of cement concrete rural roads has gained momentum due to the central government sponsored program called Pradhana Mantri Gram Sadak Yojana (PMGSY). In the present study, nine cement concrete test stretches constructed under PMGSY connecting many villages in Hangal Taluk, Haveri District at Karnataka state in India were selected, to develop the present serviceability index (PSI) equation for cement concrete roads. To carry out the studies, the test stretches were selected with varying pavement conditionsfromworst to good condition. The unevenness of the pavements was determined using MERLIN, and the unevenness values were expressed in terms of BI and are measured in millimeters per kilometer. The distress such as scaling (ravelling), spalling, faulting, cracking, rut depth etc weremeasured and recorded. The rating studies were also carried out by constituting two panels with five raters in each panel. The ratings of the two panels from the field observations were subjected to error elimination viz leniency error and central tendency error. The true ratings were obtained after application of corrections to the individual ratings. The distress data and the mean of the corrected ratings of both visual and ride ratings were used and regression analysis were carried out to develop PSI equations for these village roads based on both visual and ride ratings. The pavement distress data were substituted in the developed models to obtain the PSI values. Then the values were compared with the corrected PSR values obtained from rating studies. The pavement condition index (PCI) values were computed by the deduct value method. Key Words: Present Serviceability Index (PSI), Pavement Condition Index (PCI), International Roughness Index (IRI), Present Serviceability Rating (PSR) 1. INTRODUCTION Road transport is an important mode of transport having many desirable characteristics such as flexibility, door to door service and accessibility to remote areas. The development of distress in a road leading to failure can be considered as a continuation of the development of the irreversible strain in the road, after a period of initial compaction due to repeated loadingofoneormore elements of the road above critical values of stress and strain. The service life of the pavement would be reached when any one of the structural or functional parameters reach a minimum acceptable level,atthatpointmaintenance would be carried out and a new service life would begin. Pavement condition consists of four main components; riding comfort, load carrying capacity, safety and aesthetics. Pavement condition data are collected to assist in making decision on highway maintenance, rehabilitation and reconstruction. For successful maintenance of the pavements it is essential to know the present condition of the pavement to withstand the design traffic under prevailing climate and environmental conditions. Whenpavementsaresubjected to increased magnitude of wheel loads the pavement deterioration starts earlier than the anticipated design life. The PSI models and PCI help to analyze the present condition and to provide a proper maintenance to the pavements to increase the service life. 1.1 Location of the Roads selected for studies: In the present study, nine different rural road stretches connecting many villages in Hangal Taluk, Haveri District at Karnataka State in India wereselected.These nine stretches were further divided into 20 subsections for more precise studies. The different technologies were adopted for the construction of these roads. These roads were constructed by under the initiation of Central Government programme ‘Pradha Mantri Gram Sadak Yojana’ (PMGSY). The roads which are considered for this study comes under PMGSY and constructed as experimental stretches with varying the construction material and to evaluate their performance, durability and cost. The carriageway width of each road was found to be 3.75 m. Fig 1: Map showing the project Location & projectroads.
  • 2. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 04 Issue: 03 | Mar-2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1916 2. FIELD STUDIES 2.1 Selection of the Test Stretches The locations of the test stretches are given in Table 1 Table 1: Selected Test Stretches for Rating Studies Name of the Road Technology Adopted Age of pavement in months Gopapura (Ch 0.0 - 2.0km) CC Pavement 27 Gopapura (Ch 2.0 - 4.5km) CC Pavement 27 Dommanala (Ch 0.0 - 3.0km) CC Pavement 27 Dommanala (Ch 3.0 - 6.3km) CC Pavement 27 Shringeri (Ch 0.0 - 3.0km) CC Pavement 24 Shringeri (Ch 3.0 – 5.9km) CC Pavement 24 Multhalli (Ch 0.0 – 2.5km) RCCP 31 Multhalli (Ch 2.5 – 5.0km) RCCP 31 Multhalli (Ch 5.0 – 7.9km) RCCP 31 Yaliwala (Ch 0.0 – 3.6km) RCCP 25 Avalagerikoppa(Ch0- 2.5km) RCCP 32 Avalagerikoppa(Ch2.5- 5km) RCCP 32 Avalagerikoppa(Ch5- 7.0km) RCCP 32 Kondoji (Ch 0.0 – 2.0km) RCCP with Flyash 27 Kondoji (Ch 2.0 – 3.8km) RCCP with Flyash 27 Suraleshwara(Ch 0 - 2.0km) RCCP with Flyash 32 Suraleshwara(Ch 2 - 4.2km) RCCP with Flyash 32 Basapur (Ch 0.0 – 2.5km) RCCP with Flyash 27 Basapur (Ch 2.5 – 5.0km) RCCP with Flyash 27 Basapur (Ch 5.0 – 7.9km) RCCP with Flyash 27 2.2 Pavement Condition Measurement  Pavement Unevenness - Pavement unevenness that affects the vehicle operating cost was measured using a MERLIN. The Merlin is a simple roughness measuring machine that has been designed for use in developing countries. “Merlin stands for – a Machine for Evaluating Roughness using Low-cost Instrumentation” (Cundill, 1991). Roughness in terms of the Merlin scale, D, is obtained by first plotting the recorded data onto a histogram. Five percent of the total number of recorded observations is counted in from each end of the distribution with the position marked. The value D, in millimetres, is then obtained by measuring the distance between the two marked points, representing a data spread of 90% for the collected data. Cundill (1991) a data spread of 90% produced the highest coefficient of determination (R2), when linear regression was undertaken using D values derivedfrom different data percentages. Readers are referred to Cundill, (1991) for a more detailed description on how to obtain D, roughness in terms of the Merlin scale.With the value of D obtained, IRI by correlation can be calculated from Equation given below. IRI = 0.593 + 0.047D Where IRI is the roughness in terms of the International Roughness Index, measured inmetresper kilometre (m/km), and D is the roughness in terms of the Merlin scale, measured in mm.  Scaling - The scaling surface is identified by the worn out or the loss of material on the top of the surface. The scaled surface is measured by converting the scaled surface into suitable geometric area for measurement using tape. The scaled surface is expressed in sq meters.  Faulting Measurement - A three meter straight edge was used to determine the difference in height between the two slabs. The straight edge was placed on the longitudinal direction and faulting depth was measured with the aid of wedge scale.  Cracked Area - Crack is defect in the pavement surface, which weakens the pavement structure. It allows water to percolate into the pavements which causes further damage. These cracks were measured in terms of its spread over area. Table 2: Roughness and Pavement Distress Data of Selected Pavement Test Stretches Stretch No IRI m/km Cracking in sq m Scaling in sq m Faulting in mm 1 3.62 0.15 12.56 2.2 2 3.52 2.54 82.24 4.1 3 8.22 1.87 10.16 7.5 4 6.53 1.8 156.33 14.2 5 7.61 6.9 8.26 3.5 6 6.81 5.8 29.81 6.2 7 8.75 2.2 137.65 11.2 8 8.26 2.4 188.84 10.8 9 8.04 2 166.82 18.2 10 7.61 2.12 97.87 12.8 11 6.89 1.3 127.63 8.6 12 6.34 0.72 116.65 9.2 13 6.15 1.3 118.21 14.8 14 6.69 1.67 106.42 7.4 15 6.37 0.6 142.48 6.8 16 6.60 6.5 138.71 14.2 17 6.24 1 143.45 10.8 18 6.55 3.68 145.17 24.2 19 6.85 6.4 87.09 12.6 20 6.77 4.7 69.76 18.9
  • 3. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 04 Issue: 03 | Mar-2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1917 2.3 Constituting of Rating Panel To carry out the PSR studies, three panels were constituted viz highway, mixed and non highway panels, with five raters in each panel. 2.4 Visual Rating and Ride Rating The members of the rating panel were trained to walk along the left and right wheel paths on the selected stretches and the condition of the pavement was assessed based on the visual judgment of surface characteristics. The rating scale adopted forthevisual assessmentofpavementis shown in Table 3. Based on the condition of the stretch the raters were allowed to rate the stretch on the scaleofzeroto five. For the rating by riding technique the raters were taken in a test vehicle driven along the stretches and are trained to assess the PSR value according to comfort condition. The rating scaleadoptedfortheridingassessment of pavement is shown in Table 4. Table 3: Description of Visual Rating Scale Sl. No Description Based on Visual Condition of Pavement Surface Numerical Scale 1 Perfectly even surface, without undulations, cracking, patching or scaling. 4-5 2 Slightly uneven surface with some undulations, Slight cracking. 3-4 3 Moderately uneven surface, medium cracking, slight faulting or scaling. 2-3 4 Uneven surface with improperly patched potholes, medium to heavy crackingandscaling 1-2 5 Uneven surface with different type of undulation, potholes, heavy cracking 0-1 Table 4: Description of Riding Rating Scale Sl. No. Description Based on Riding Condition of Pavement Surface Numerical Scale 1 Without discomfort, perfect smoothness 4-5 2 Little distortion, fairly smooth riding 3-4 3 Medium distortion, fair to uneven riding 2-3 4 Heavy distortion, uncomfortable riding 1-2 5 Intolerable, very discomfortable riding 0-1 The following three panels were constituted for rating the ride and visual ratings:  Highway panel- The panel consisting of 5 members having knowledge about highway technology.  Non highway panel – The panel consisting of 5 members of non highway background.  Mixed panel- The panel consisting of both the members of above two panels. 3. ANALYSIS OF DATA 3.1 Analysis of Present Serviceability Ratings The compiled data were analyzed to estimate the mean and standard deviation of rating values. The rating values were thus determined foreachpavementsectionboth for ride and visual rating techniques. The same data were analyzed for the estimation and removal of errorspresent in the rating. The statistical analysis was carried out for the compiled data thus the values of mean and standard deviation of each pavement section and for each rater is calculated. 3.2 Estimation and Removal of Errors in the Individual Present Serviceability Ratings The members rated the selectedstretchesboth by visual and ride rating techniques as per the scale mentioned in the above tables. But the ratings were modified due to the following errors:  Leniency error - The leniency error was determined by computing mean of individual rater and mean of all single ratings and the leniency error is obtained by subtracting mean of the individual rater and mean of all single ratings.  Central tendency error - The central tendency errors are determined by calculating the standard deviation of individual raters and standard deviation of all single ratings. 3.3 Determination of True Ratings After the determination of errors in the visual and ride rating values for all the three panels the true present serviceability ratings were determined. The true ratings were obtained by adding corrected leniency error and central tendency error to the field ratings. The mean of the three panels and the overall mean value both for visual and ride rating are given in Table 5 and Table 6. Table 5: Corrected Values of the Visual Ratings for Selected Pavement Test Stretches Stretch No Highway Mixed Non highway Average 1 4.2 4.0 4.0 4.06 2 4.0 3.7 3.3 3.70 3 3.2 3.0 3.5 3.23 4 2.3 2.5 2.3 2.36 5 3.6 3.6 3.6 3.59 6 3.4 3.6 3.8 3.62 7 2.7 2.8 3.2 2.92 8 2.9 2.9 3.0 2.94 9 2.1 2.4 3.1 2.49 10 3.4 3.3 3.4 3.37 11 2.9 2.9 3.4 3.07 12 2.9 3.1 3.5 3.16 13 2.8 3.1 3.3 3.05 14 3.1 3.1 3.5 3.22 15 3.1 3.0 3.1 3.05 16 2.8 2.9 3.0 2.90 17 3.0 3.2 3.1 3.08 18 1.8 1.6 2.4 1.92 19 2.2 2.1 3.1 2.45 20 2.6 2.9 3.4 2.92
  • 4. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 04 Issue: 03 | Mar-2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1918 Table 6: Corrected Values of the Ride Ratings for selected pavement test stretches Stretch No Highway Mixed Non highway Average 1 4.2 4.1 4.0 4.13 2 3.8 3.4 3.9 3.71 3 3.7 3.9 3.3 3.61 4 2.4 3.0 2.4 2.60 5 3.4 3.6 3.6 3.55 6 3.3 3.7 3.5 3.51 7 2.6 2.9 3.0 2.82 8 2.6 3.2 3.2 2.98 9 1.9 2.9 3.1 2.64 10 3.2 3.2 3.5 3.30 11 3.3 3.4 3.5 3.41 12 3.5 3.3 3.8 3.54 13 3.0 3.4 3.2 3.19 14 3.2 3.4 3.2 3.27 15 2.7 3.0 3.4 3.02 16 2.9 3.0 3.1 2.99 17 2.8 2.9 2.7 2.85 18 1.5 1.7 2.5 1.88 19 2.2 2.4 3.2 2.61 20 2.8 2.8 3.5 2.99 3.4 Development of Model The PSI model was developed along the lines of AASHO equation using the field measurements such as faulting, IRI, scaling and cracking and mean PSR values. The PSI equation was developed for both the visual and ride rating using analysis tool pak and regression analysis. The PSI models were developed for each of individual distress and finally a multiple linear regression analysis was carried out to develop models. The models are given below Chart-1: The linear graph representing PSR v/s IRI values. PSI = 4.11 - 0.152 (IRI) PSI model with roughness as a distress expressed in IRI Chart-2: The linear graph representing PSR v/s Faulting values. PSI = 3.92 – 0.076 (faulting) PSI model with faulting as a distress expressed in mm (average) per 100 m2 area. Chart-3: The linear graph representing PSR v/s Scaling values. PSI = 3.73 - 0.0062 (Scaling) PSI model with scaling as a distress expressed in square meter per 100 sq m area. Chart-4: The linear graph representing PSR v/s cracking values. PSI = 3.19 - 0.037 (cracking) PSI model with cracking as a distress expressed in square meter per 100 sq m area. 3.4.1 Multiple Linear Regression Model for Visual Ratings Model: Yi (PSI) = β0+β1 (IRI)+β2 (Faulting) +β3 (Scaling) + β4 R(Cracking) + εi
  • 5. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 04 Issue: 03 | Mar-2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1919 Assumptions 1) The response variable Y is linearly related to regressor variables IRI, cracking, faulting, scaling and 2) εi (error) i.e. errors are normal with mean zero independent common variance σ2 . Model for Visual Rating of Pavements PSI = 4.31 – 0.039 (IRI) – 0.055 (faulting) - 0.003 (scaling) – 0.025 (cracking) PSI = Present serviceability Index for the range 0.00 to 5.00 IRI = International Roughness Index,m/kminthe range3.52 to 8.75 m/km Faulting = faulting in mm in the range 2.2 to 24.2 mm (average) per 100 m2 area. Cracking = Cracking, in the range 0.15 to 6.9 square meter per 100 sq m area Scaling = Scaling in the range of 8.26 to 188.84 square meter per 100 sq m area. Table 7: Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate Observations 1 0.893 0.798 0.745 0.252 20 A Predictors: (Constant), IRI, Faulting, Cracking and Scaling B Dependent Variable: PSR R2 =0.798 tells us that 79.8% of variation in Y is explained by the regressor variablesIRI,Cracking,FaultingandScaling. (Note: anything more than 60% is good) Table 8: Anova Model Sum of Squares Df Mean Square F 1 Regression 3.783 4 0.945 14.88 Residual 0.953 15 0.063 Total 4.730 19 Coefficients Standard Error t Stat P-value Intercept 4.313 0.305 14.12 4.53E-10 IRI -0.039 0.048 -0.81 0.427 Cracking -0.025 0.031 -0.79 0.439 Faulting -0.055 0.013 -4.11 0.0009 Scaling -0.003 0.001 -2.09 0.053 3.4.2 Multiple Linear Regression Model for Ride Ratings Model: Yi (PSI)= β0+β1 (IRI) +β2 (Faulting) +β3 (Scaling) + β4 R (Cracking) + εi Assumptions 1) The response variable Y is linearly related to regressor variables IRI, cracking, faulting, scaling and 2) εi (error ) i.e. errors are normal with mean zero independent common variance σ2 . Model for Ride Rating of Pavements PSI = 4.37 – 0.022 IRI – 0.051(faulting) - 0.003(Scaling)– 0.050(cracking) PSI = Present serviceability Index for the range 0.00 to 5.00 IRI = International Roughness Index, m/km in the range 3.52 to 8.75 m/km Faulting = faulting in mm in the range 2.2 to 24.6 mm (average) per 100 m2 area. Cracking = Cracking, in the range 0.15 to 6.9 square meter per 100 sq m area. Scaling = Scaling in the range of 8.26 to 188.84 square meter per 100 sq m area. Table 9: Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate Observations 1 0.901 0.812 0.762 0.245 20 A Predictors: (Constant), IRI, Faulting, Cracking and Scaling B Dependent Variable: PSR R2 =0.90 tells us that 90% of variation in Y is explained by the regressor variables IRI, Cracking, Faulting and Scaling. (Note: anything more than 60% is good) Table 10: Anova Model Sum of Squares Df Mean Square F Sig. 1 Regression 3.898 4 0.974 16.23 2.52E-05 Residual 0.900 15 0.060 Total 4.798 19 Coefficients Standard Error t Stat P-value Intercept 4.37 0.296 14.75 2.45E-10 IRI -0.022 0.046 -0.485 0.634 Cracking -0.050 0.030 -1.652 0.119 Faulting -0.003 0.013 -3.881 0.001 Scaling -0.051 0.001 -2.704 0.016 3.4.3 Determination of Pavement Condition Index (PCI) The PCI was calculated according to ASTM Standards. PCI =PCI max - ∑ Deduct PCI – individual condition index based on measured condition. PCI max = value for perfect condition with no measured defects =100 Deduct – deduct value assigned to distress type severity and extent. Table 11: The PCI Values and Condition of Pavement for Selected Stretches. Stretch No Cracking Faulti ng Scaling TDV CDV PCI Condition 1 0.15 2.2 12.56 2 2 98 good 2 2.54 4.1 82.24 26 15 85 Satisfactory 3 1.87 7.5 10.16 16 9 91 Good 4 1.8 14.2 156.33 71 46 54 Poor 5 6.9 3.5 8.26 15 8 92 Good 6 5.8 6.2 29.81 24 16 84 Satisfactory 7 2.2 11.2 137.65 63 41 59 Fair 8 2.4 10.8 188.84 74 49 51 Poor 9 2 18.2 166.82 77 51 49 Poor 10 2.12 12.8 97.87 60 38 62 Fair 11 1.3 8.6 127.63 35 23 77 Satisfactory 12 0.72 9.2 116.65 56 37 63 Fair
  • 6. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 04 Issue: 03 | Mar-2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1920 13 1.3 14.8 118.21 62 41 59 Fair 14 1.67 7.4 106.42 52 33 67 Fair 15 0.6 6.8 142.48 58 36 64 Fair 16 6.5 14.2 138.71 44 27 73 Satisfactory 17 1 10.8 143.45 64 42 58 Fair 18 3.68 24.2 145.17 82 53 47 Poor 19 6.4 12.6 87.09 73 48 52 Poor 20 4.7 18.9 69.76 64 40 60 fair 4. CONCLUSIONS The following conclusion has been drawn from the study:  The pavement selected varies from worst to good condition covering the boundaries for the model. The PSI models developed for visual and rideratingshows IRI has a major influence on the PSI model and in ride rating model faulting has also a major influence compared to scaling and cracking. For Visual rating studies, PSI=4.31–0.039(IRI)–0.055(faulting)-0.003(scaling) –0.025(cracking) For Ride rating studies. PSI=4.37–0.022 IRI–0.051(faulting) -0.003(Scaling) –0.050(cracking)  The IRI and age has a great influence on the PSI. PSI starts decreasing with increasing in age and IRI.  Based on the remaining service life the lowest service life is 140 months and highest is 148 months. According to this the concrete rural roads provide better service upto 12 years based on roughness. Using the Pavement Condition Index (PCI) values by deduct value method 3 stretches were found to be in good condition, 4 were satisfactory, 8 were in fair condition and remaining 5 in poor condition. The plain cement concrete roads have given better performance compared to the other roads constructed using different materials and different technologies. Hence the plain cement concrete pavements are in good condition and the other pavement conditions vary from satisfactory to poor condition. REFERENCES 1. Carey, W.N. And Irick, P.E., “The Pavement Serviceability – Performance Concept”, HRB Bulletin 250, 1960. 2. Hans Prem and Geoff Ayton “Improved Techniques for Assessing Ride Quality on Concrete Pavements”. 3. Hutchinson, B,G., “Priniciples of Subjective Rating Scale Construction”, Highway Research Record No 46, Highway Research Board, Washington D. C., 1964. 4. Chenung M .S and Kyle B R “Service life prediction of concrete structures by reliability analysis” architectural and engineering services, public worksandgovernment services Canada 5. Prozzi,J.A., and Madanat, S.M., “DevelopmentofPavement Performance Models by Combining Experimental and Field Data”, Journal of Infrastructure systems,American Society of Civil Engineers, 9-22, 2004 6. Phull, Y.R., “Evaluation of Pavement Condition by Serviceability Rating Technique”, Seminar on Strengthening of Existing Road Pavement, IRC Publication, 1971. 7. Dossey and Hudson “Performance Prediction Models for Rigid Pavements” in Texas. 8. Susan Rose, Kuncheria.P.Issac, and Binu Sara Mathew., “Development of Deterioration Models for Rural Roads”, College of Engineering, TVM, India, 280-284, Oct 2007 9. Jorge A. Prozzi and Samer M.Madanat., “Incremental Nonlinear Model for Predicting Pavement Serviceability”, Journal ofTransportationEngineering,AmericanSociety of Civil Engineers, 635-641, 2003 10. ASTM Designation: D6433 – 11 “Standard Practice for Roads and Parking Lots Pavement Condition Index” 11. Bandara N. and Gunarathne.M., “Current and Future Pavement Maintenance Prioritization Based on Rapid Visual Condition Evaluation”, Journal of Transportation Engineering, American Society of Civil Engineers, 116- 123, 2001. 12. M.A Cundill, “The MERLIN low-cost road roughness measuring machine”, TransportationandRoadResearch Laboratory Research Report 301. 13. Dr. L.R. Kadiyali, Dr. N.B. Lal “Principles and Practices of Highway Engineering”, Khanna Publishers. New Delhi. 14. Indian Roads Congress. “Journal of the Indian Roads Congress Volume 68-1” Published by the IRC New Delhi April-June 2007. 15. Greggory Morrow. “Comparison of RoughnessMeasuring Instruments” Department of Civil and Environmental Engineering University of Auckland Private Bag 92109 Auckland New Zealand May 2006.