International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 3, Issue 2, February 2014
ISSN 2319 - 4847
Defining Pavement Condition States to Quantify
Road Quality for Designing of Pavement
Maintenance Management System
S. R. Katkar 1 and P. P. Nagrale 2
1
S. R. Katkar is a Research Scholar at Department of Civil Engineering, Sardar Patel College of Engineering, Andheri,
Mumbai, India and Assistant Engineer Class 1 at PWD, Govt. of Maharashtra.
2
Dr. Prashant P. Nagrale is a Associate Professor at Department of Civil Engineering, Sardar Patel College of Engineering,
Andheri, Mumbai, India.
Abstract
Important factor in the Pavement Maintenance Management System (PMMS) is to quantify the quality of pavement. Earlier
research is based on various parameters such as pavement condition index (PCI), mechanistic properties, and physical distress.
Ultimate objective of Maintenance Management System (MMS) is to optimize the resources required to upgrading that utility. In
none of the previous research; the relationship between pavement condition and corresponding maintenance cost has been
considered in designing of MMS. In this study, 70 pavements are studied and an attempt is made to categories them in various
condition states based on their repair cost. The research is useful in designing of cost effective MMS.
Keywords: PMMS, PCI, MMS
1. INTRODUCTION
Pavement distress information is usually converted into a condition index. The condition index combines information
from all of the distress types, severities, and quantities into a single number. This number can be used at the network level
to define the condition state, to identify when treatments are needed, for ranking or prioritization, and to forecast
pavement condition. The condition index may represent a single distress such as fatigue cracking or a combination of
many pavement distresses which is then usually referred to as a composite index. Additional information has also been
included in some indices such as traffic levels, highway class, etc. to produce priority ranking indices.
Most of the pavement maintenance prediction models of mechanistic or imperial mechanistic type are data centric in
nature. It is possible that different pavements having
Same PCI require different repair policies corresponding to different repair cost. Ultimately it is not possible to predict
the exact pavement condition from pavement condition index (PCI). The data base includes detailed experimental
analysis and observations of each of pavement stretches.
India is having 3.34 million kilometers of road network out of which 3.14 million kilometers are rural roads. India’s rural
& urban roads network is the second largest in the world. For designing of PMMS, pavements in which there is a change
in any of the distress causing parameters need to be analyzed separately. Obtaining the data of individual distresses, for
huge road network is a cumbersome task. Indian Road Congress special publication SP: 72 - Guidelines for inspection
and maintenance of rural roads, is the first organized step in this direction. In absence of detailed data of distress indices,
defining condition state of pavement is a key factor in designing proper PMMS.
2. LITERATURE REVIEW
Review of Literature is carried out to understand the detailed descriptions of pavement rutting and scoring processes. The
major contribution is found to be done by National Highway Institute which has developed a methodology of calculating
pavement distress indices. Pavement condition index, pavement scores used by the Washington State DoT and the
Vermont DoT are also studied who have a major contribution in PMMS designing.
2.1 National Highway Institute (NHI): NHI’s pavement management system (1998) has summarized the pavement
distresses into indices that include trigger treatments, life-cycle costs and network condition. There is a variation in the
distress values and corresponding indices from state to state. The extent and severity of pavement distress is used for
computing deduct values. In rating distresses there are two approaches: (1) to use the most dominant distress type present,
and (2) to use all the types of distresses present. For each distress type/severity level, failure criteria are established in
terms of the distress index that indicates the need of rehabilitation.
2.2 American Society for Testing of Materials (ASTM): Pavement Condition Index (PCI) developed by the U.S. Army
Corp of Engineers has been accepted by ASTM for PMMS (PAVER, 1997). For each distress and severity level present,
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Volume 3, Issue 2, February 2014
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the area affected is added up and divided by the area of the sample unit, which expressed in percentile, is referred to as
distress density. Subsequently, deduct values are computed for each distress density, using a series of charts.
2.3 Washington State Department of Transportation (WS DoT): WS DoT initially utilized the Present Condition Rating
(PCR) as a means of rating pavement condition (Kay et al. 1993). PCR of newly constructed pavement is considered as
100 and PCR of pavements under observations are calculated by deducting assigned distress values for the extent and
severity of each distress viz. alligator cracking, longitudinal cracking, transverse cracking, patching, spalling at joints and
cracks, faulting settlement etc.
2.4 Vermont Department of Transportation (V DoT): The earlier version of the Vermont DoT pavement management
system (Deighton et al. 1995) utilized discrete deduct values for structural cracking (traffic-related) and transverse
cracking (non-traffic related) and continuous deduct values for rut depth and roughness. Individual indices were defined
for each of these distress types. Introduction of an automated distress data collection system in 2001 provided additional
distress data. As a result, a modified procedure emerged for computing deducts involving continuous deduct functions for
cracking as a function of extent, for three discrete severity levels (Deighton & Associates 2005). These are derived using
log-log plots of deduct values versus extent for multiple distresses of various extents, the deduction of each of them is
computed individually, then their deduct is computed for those of a certain extent combined and the maximum deduct
value is used for computing the overall distress index.
Earlier research is done considering various methods of quantifying the pavement quality such as Pavement Condition
Index (PCI), mechanistic properties, severity of physical distress. Such research is useful for PMMS which aims at
maintaining road network at its best usability without economical considerations. These systems fail where limited
financial resources are available, such as in developing countries like India, where priority sector is not road but are
agriculture, education, health, mass housing etc. The ultimate objective of any maintenance management systems is to
optimize the resources, mainly money, required to minimize the maintenance cost. But in previous research works
relationship between pavement conditions and respective maintenance cost not established by any researchers.
3. OBJECTIVE OF THE RESEARCH
The objective of this study is to develop the most appropriate and simple technique of defining the pavement condition
state in the absence of detailed data of distress indices. For which historic development of pavement condition indices are
studied and limitations of the same are reviewed. Typical construction procedure used by Public Works Department of
Maharashtra state is also studied. Average pavement life of bituminous rural road is observed as 14 years and hence the
entire life span of a typical bituminous road is divided into 7 equal periods of 2 years each. Maintenance cost for such
several pavement stretches in single lane rural road network is calculated for each of 2 year span of observation. Average
repair cost required for improving quality of road stretch in various condition states is then identified from this data.
Based on this observations condition states are defined in to 7 categories – from 7 (new condition state) to 1 (poor
condition needing immediate total rehabilitation). For rate analysis, District Schedule Rates of Public Work Department,
Pune 2012-13 is referred. The relationship between condition states of pavement v/s corresponding maintenance cost is
plotted that helps to predict the nature of deterioration. This technique helps to find the right maintenance policy at
particular time period and corresponding cost. This model provides guideline for identifying pavement distress types and
defining the levels of severity and extent (area, length, count) associated with each distress.
4. STUDY M ETHODOLOGY AND ASSUMPTIONS
For designing the PMMS model, authors have considered one kilometer stretch as one pavement in which it is assumed
that all properties viz. traffic intensity, subgrade soil, annual rain fall intensity and impact load etc. remain constant.
Carriageway of single lane is considered. Construction technology used for new road is explained in Table 1 which is a
base to find out the repair cost of pavement
Table 1: General Construction Practice to Construct New Rural Road by PWD, Maharashtra State, India
Name of layer
Blanketing
Metal Size
Hard murum
having plasticity
index < 6.0
Layer thickness
Percentage of filler
150 to 600 mm required to
achieve 15 CBR of subgrade.
Nil
60mm
150mm
Hard murum = 1/3 or 33% of metal
WBM
(oversize)
WBM (normal
size)
BBM
OGC
40mm
75mm
Soft murum = 1/4 or 25% of metal
12 and 40 mm
10 & 12 mm
75mm
20 mm
Seal coat
6 mm
Nil
Bitumen weighting 2 Kg / sqm.
Bitumen weighting 1.46 Kg / sqm.
Bitumen weighting 0.75 to 0.98 Kg /
sqm.
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5. DATA COLLECTION AND ANALYSIS
Pavements up to two year old are considered to fall in condition state 7. For next two year interval i.e. pavements having
age from two to four years are considered to fall in condition state 6. Similarly pavement having age twelve to fourteen
years fall in condition state 1 pavement. For each of such pavement condition states, 10 samples are observed.
Maintenance treatment required to keep / bring it to satisfactory working condition is tabulated in Table 2.
Table 2: Maintenance treatment for roads in various condition states
Age of
Pavem
ent
(years)
2
4
6
Maintenance treatment required in terms of % of total area of pavement
Type of
treatment
Seal Coat
OGC +
Seal Coat
OGC +
Seal Coat
BBM 50 mm
8
10
12
14
Pavement samples
Avg.
treatment
1-7
8-14
15-21
22-28
29-35
36-42
43-49
50-56
57-63
64-70
35
30
36
35
30
35
30
35
30
35
33.1
45
40
50
45
40
45
40
45
40
50
44
100
100
100
100
100
100
100
100
100
100
100
2
1
1
1
1
1
2
1
1
1
1.2
OGC +
Seal Coat
100
100
100
100
100
100
100
100
100
100
100
BBM 50 mm
45
50
50
55
40
52
40
45
45
50
47.2
OGC +
Seal Coat
100
100
100
100
100
100
100
100
100
100
100
BBM 75 mm
100
100
100
100
100
100
100
100
100
100
100
25
26
22
20
25
25
28
28
22
20
24.1
100
100
100
100
100
100
100
100
100
100
100
BBM 75mm
100
100
100
100
100
100
100
100
100
100
100
WBM 40mm
100
100
100
100
100
100
100
100
100
100
100
WBM 60mm
38
30
38
35
30
35
40
35
30
38
34.9
OGC +
Seal Coat
100
100
100
100
100
100
100
100
100
100
100
BBM 75 mm
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
30
31
28
26
27
29
28
30
25
26
28
WBM 40
mm
OGC +
Seal Coat
WBM 40
mm
WBM 60
mm
Blanketing
Where,
OGC=Open Graded Carpet.
BBM = Bituminous Bound Macadam.
WBM = Water Bound Macadam.
On the basis of this data, rate analysis is carried out w.r.t. District Scheduled Rates (DSR 2012-13) of Pune, Maharashtra,
which is summarized in Table III. A relation between condition indices and pavement maintenance cost is studied.
Pavement stretch is of 1Km with 3.75 m width. Surface area of each pavement = 1 x 1000 x 3.75 = 3750 sqm.
With respect to tables 2 and 3, the repair cost is calculated as below: e.g. for 1st row = (Rate / sqm) x (area of pavement in sqm ) x (% area of the treatment required)
= 79.17 x 3750 x 0.33 = ₹ 97,973/Table 3: Rate Analysis for different pavement condition states
Cost of treatment in ₹ (Figure in the bracket shows % area of the treatment required)
Blanketin
g
@ 232.35
/ sq.m.
WBM
60mm
@ 299.45/
Sqm
WBM
40mm
@157/
Sqm
BBM
75mm
@313.40/
Sqm
BBM
50mm
@269.5
5/ Sqm
OGC +
Seal Coat
@247.55/
Sqm
Seal coat
@79.17/
Sqm
2
-
-
-
-
-
-
97,973
(33%)
97,973
4
-
-
-
-
-
4,12,635
(44%)
-
4,12,635
Conditio
n state
Age of
Pavement
7
6
Volume 3, Issue 2, February 2014
Total cost in
₹
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Volume 3, Issue 2, February 2014
ISSN 2319 - 4847
5
6
-
-
-
4
8
-
-
-
3
10
-
-
2
12
-
1
14
2,43,968
(28%)
3,93,028
(35%)
11,22,938
(100%)
1,43,066
(24%)
5,88,750
(100%)
5,88,750
(100%)
5,52,367
(47%)
11,75,250
(100%)
11,75,250
(100%)
11,75,250
(100%)
10,108
(1%)
-
9,28,313
(100%)
9,28,313
(100%)
9,28,313
(100%)
9,28,313
(100%)
9,28,313
(100%)
-
9,38,421
-
14,80,680
-
22,46,629
-
30,85,341
-
40,59,219
Different Condition States of Pavements: Based on the type of treatment and age of pavement, in this paper, condition
state description of the pavement is defined as mentioned in the Table 4.
Table 4: Different condition state descriptions of the pavements
Condition
state
Description
7
New condition: Routine maintenance & Nominal Seal coat within DLP.
6
Good condition: Pot hole filling by OGC with seal coat material.
5
Generally good condition: Potential exists for minor maintenance (Nominal BBM pot hole filling & OGC with
seal coat).
4
Major maintenance required: 40 - 50% BBM & OGC with seal coat.
3
Minor rehabilitation required: Pot hole filling by normal size metal, BBM layer & OGC with seal coat.
2
Marginal condition: Potential exists for major rehabilitation (Pot hole filling by Oversize metal layer, Normal size
metal layer, BBM layer & OGC with seal coat).
1
Poor condition: Rehabilitation required immediately. (Repairing by partially blanketing, Over size metal layer,
Normal size metal layer, BBM layer & OGC with seal coat).
Figures below are the onsite photographs of various types of road pavements and are indicative of their condition states.
Figure 1: Road pavement of Alegaon Malthan road MDR – 81 CH 13.00 section in condition state 7.
Figure 2: Road pavement of Boribel to Deulgaon Raje MDR – 80KM 37.00 section in condition state 6.
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Figure 3: Road pavement of Malthan Bhigwan Road MDR – 79KM 2.800 section in condition state 5.
Figure 4: Road pavement of Rajegaon Khanota MDR – 46KM 2.100 section in condition state 4.
Figure 5: Road pavement of Daund Gar Dapodi MDR – 82 section in condition state 3.
Figure 6: Road pavement of Daund Gar Dapodi section in condition state 2.
Figure 7: Road pavement of Daund Gar Dapodi in condition state 1.
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The relationship between pavement condition state v/s corresponding maintenance cost is plotted as shown in “Figure 8”.
It helps predict the nature of deterioration.
Figure 8: Maintenance cost (in lakh of ₹) of pavement corresponding to condition state.
From “Figure 8,” it is observed that the rate of distress leading to deterioration in terms of cost of pavement repair
exponential in nature i.e. as time increases the maintenance cost of pavement also increases substantially. It is important
to identify trigger treatment to optimize the cost of maintenance treatment at proper time. It is observed that the increase
in cost of repair from condition state 7 to 6 is ₹ 3,14,662/-, while that of from condition state 2 to 1 is ₹ 9,73,878/-. It
clearly indicates that the delay in the repair treatment leads to substantial increase in the repair cost.
6. CONCLUSION
Quantification of qualitative data is important for any infrastructure maintenance management system. From historical
research, it is observed that distressed value is used to identify pavement condition index. As PCI defines the
cumulative distress, it doesn’t give exact condition of pavement and its corresponding repair cost.
1. The study results provide a simple and realistic technique to categories condition state of pavement with respect to
repair cost as mentioned in table no. 2 and 3.
2. Pavement condition state descriptions along with deterioration process, corresponding cost of repair and relation of
pavement age with repair cost are established.
3. Pavement condition states are defined into 7 to 1 for quantification of quality of pavement, 7 being ‘new condition’
while 1 being ‘poor’.
4. Cost of repair required by pavements in each of the categories is found out and is mentioned in table no. 3.
5. Relationship between repair cost of pavement and time is exponential in nature.
The increase in cost of repair from condition state 7 to 6 is ₹ 3,14,662/-, while that of from condition state 2 to 1 is
₹ 9,73,878/-. The observation fetch attention of importance of trigger treatment to immediately start before it is late.
7. FUTURE SCOPE
The analysis procedure adopted in this study is to get condition state of particular pavement from the corresponding
maintenance cost. As the relationship between them is non linear, linearly interpolation of cost is not possible to find
condition state of pavement. It is therefore necessary to establish the relationship between condition state of pavement and
cost of repair.
References
[1] Deighton R., J.Sztraka(1995), "Pavement Condition dTV Technical Guide,” Vol.3 Deighton and Associates Ltd,
Bowmansville, Ont., July 1995.
[2] Indian Road Congress special publication (2007), SP: 72-Guidelines for the Design of Flexible Pavements for Low
Volume Rural Roads.
[3] Jackson, N.C. (2008), “Development of Revised Pavement Condition Indices for Portland Cement,” Washington
State Department Concrete Pavement for the WSDOT Pavement Management System of Transportation, Olympia
WA, WA-RD 682.3, Nov. 2008.
[4] Katkar S., Nagarale P. (2013), “Formulation of Group of Pavements by Using Latin Square ANOVA Required for
Network Level Pavement Maintenance Management,” IEI PLC Journal, Vol.37, pp. 78-83, Nov. 2013.
Volume 3, Issue 2, February 2014
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
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[5] Katkar S., Nagarale P. (2013), “Application of Markovian Probabilistic Process to Develop a Decision Support
System for Pavement Maintenance Management,” International Journal of Scientific & Technology Research, Vol.2
Issue 8, August. 2013.
[6] Kay R. K., Mahoney J. P, Jackson N. C. (1993), “The WSDOT Pavement Management System – A 1993 Update,”
WSDOT Report No. WA-RD-274.1, Washington State Department of Transportation, Olympia WA, September.
[7] National Highway Institute (1992). Short Course, Highway Pavements, Student Workbook Publication, Course
Number 13114 Federal Highway Administration, National Highway Institute.
[8] National Highway Institute (1998). Short Course, Pavement Management Systems, Student Workbook Publication,
Course No. 13135 Federal Highway Administration.
[9] New Vermont Agency Transportation Pavement Performance Models (2005). Phase I Report Deighton and
Associates and Applied Pavement Technology, Feb. 2005.
[10] PAVER Asphalt Distress Manual (1997). US Army Construction Engineering Laboratories, TR 97/104 and TR
97/105.
[11] Shahin M.Y., S. D. Kohn (1981), “Pavement Maintenance Management for Roads and Parking Lots,” Report No.
CERL-TR-M-294, U.S. Army Construction Engineering Research Laboratory, Champaign IL, Oct. 1981.
[12] Standard Guide for, “Conducting Subjective Pavement Ride Quality Ratings (1998),” American Society for Testing
of Materials, ASTM Book of Standards Volume 04.03, E1927-98, West Conshohocken, PA, 1998.
[13] Standard Practice for Roads and Parking Lots Pavement Condition Index Surveys (2000). American Society for
Testing of Materials, ASTM Book of Standards Volume 04.03, D6433-99, West Conshohocken, PA.
[14] Standard Specification (1964). Public Works and Engineering Department Book, Government of Maharashtra,
Mumbai 1st January 1964.
AUTHOR
S. R. Katkar is a Research Scholar at Department of Civil Engineering, Sardar Patel College of
Engineering, Andheri, Mumbai, India. He is working as Assistant Engineer Class 1 at Public Works
Department, Government of Maharashtra since 2000.
Dr. Prashant P. Nagrale is a Associate Professor at Department
Engineering, Andheri, Mumbai, India.
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