Analysis of Flexible Pavement Distress Behaviors

ANALYSIS OF FLEXIBLE PAVEMENT DISTRESS BEHAVIORS
ABSTRACT:
Abdullah Al-Mansour
Professor
Civil Engineering Department
King Saud University
Riyadh, Saudi Arabia
amansour@ksu.edu.sa
The Pavement Maintenance Management System (PMMS) of Riyadh city perform
comprehensive pavement visual survey prior to each maintenance program. In the condition survey
detailed information related to type, severity and density of existing distresses was collected. The
collected data was then used to determine needed maintenance activities on a project level. This
process was proven to be costly and very time consuming.
This paper utilizes the data of three pavement distress surveys over a period of six years to
investigate and analyze the behavior of common types of pavement distress. The analysis included
studying the factors affecting distress density. These factors included pavement condition, traffic level,
severity level and time. In addition, the analysis involved correlation between distresses and
propagation behavior and trend of common pavement distress types over time. The out put of these
analyses can be used to predict distress data prior to each maintenance program. This will eliminate
the need for comprehensive condition survey which proven to be costly and time consuming.
INTRODUCTION
Pavement distresses are visible imperfections on pavement surface. They are
symptoms of the deterioration of pavement structures. Most, if not all, agencies that
have implemented a Pavement Maintenance Management System (PMMS) collect
periodic surface distress information on their pavements through distress surveys [1].
Distress evaluation, or condition survey, includes detailed identification of pavement distress
type, severity, extent, and location. To combine these details, an index is assigned to each
pavement which is transferred to a general rating. Every highway agency either develops
pavement distress evaluation procedure or selects a developed one for its pavement condition
survey. Regardless of the size of the highway network or the sophistication of the PMS
procedure, most PMS strategies can offer assistance at two levels: the network level and the
project level. Network level information provides management with broad-based data about
the entire system. Information for planning purposes and fiscal analysis is often provided by
the network data. On the other hand, project level information can include specific details
about engineering design, construction and cost accounting. Obviously the data required for
each level differs considerably. Network level required disaggregate data that reflects the
general pavement condition. However, project level needs detailed and specific data on
expected distresses.
In general, distress density starts its propagation process very slowly, but it accelerates
more at a later stage. There are factors that affect rate of propagation. These factors may
include pavement condition, traffic levels and distress severity. The distress density
propagation on a new or recently overlaid pavement sections having excellent condition is
expected to be slower than on pavement sections with poor condition. A distress is expected
to behave differently on pavement sections subjected to different traffic levels. Also, the
distress severity levels have an effect on behavior and propagation of distress density.
The Pavement Maintenance Management System (PMMS) of Riyadh city perform
comprehensive pavement visual survey prior to each maintenance program [2]. In the condition survey,
detailed information related to type, severity and density of existing distresses was collected. The
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collected data was then used to determine needed maintenance activities on a project level. This
process is usually very expensive and time consuming.
STUDY OBJECTIVE
The main objective of this study is to investigate the behavior of the common types of
pavement distress on Riyadh’s street network. The analysis include determining factors affecting
distress density, correlation analysis and propagation behavior of common pavement distresses.
DATA BASE DEVELOPMENT
Riyadh Municipality conducts comprehensive pavement condition evaluation prior to each
maintenance program. The condition evaluation involves determining types, severity and density of all
pavement distresses. The collected data was then converted to pavement condition index called Urban
Distress Index (UDI). The UDI is a combined local index of fifteen pavement distresses developed for
the Riyadh Pavement Maintenance Management System (PMMS). The index ranges from zero to 100,
where 100 represents excellent pavement condition. The UDI is calculated based on pavement
distresses type, severity and density for a specific section. Four pavement condition rating was adopted
for the UDI system: Poor, Fair, Good and Excellent [3]. The Index has to be updated periodically to
reflect the existing pavement condition. Pavement performance models were developed to update UDI
on the network level [4]. However, the models did not predict changes in individual distress data. The
condition of individual distress over time is very essential in planning maintenance activities on a
project level.
To date three comprehensive condition surveys were accomplished. The historical data was
classified based on highway class into main and secondary streets. These surveys covered a period of
four years for main streets and six years of secondary streets. All pavement sections that received
maintenance during the analysis period were eliminated. The remaining sections along with common
distress data and date of inspection were stored in a separate data base for further analysis. The
common main street distresses included in this study were: longitudinal and transverse cracks,
weathering and raveling, potholes and depressions. The secondary street distresses included were
longitudinal and transverse cracks, weathering and raveling and potholes [5]. The analysis involved
590 main street data points and 3119 secondary street observations during each survey.
FACTORS AFFECTING DISTRESS DENSITY
Various factors may affect distress density. These factors include distress type, pavement
condition, distress severity level, traffic level and time. To determine which of these factors affect
distress density, analysis of variance was conducted. The ANOVA model has the following form
DD = DT + PC + DS + AD + T
Where,
DD = percent of distress density
DT = distress type (common types of distress)
PC = pavement condition (excellent, good, fair and poor)
DS = distress severity level (low, medium and high)
TL = Traffic level for main streets only (high and low)
T = time since first inspection in years.
The distress density is calculated as the distress area divided by the surveyed area multiplied
by 100 for all distresses. The distress quantity for longitudinal and transverse cracks, weathering and
raveling and depressions are in square meters and for potholes is number of existing potholes in
surveyed area. The output of this analysis is shown in Table 1. The analysis indicated that all factors
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have an effect on density of the individual distress in main streets and secondary streets except
severity level for potholes. Pothole is localized type of distress and its severity level is not expected
to affect the density of the distress. This is clear from the p-value of the ANOVA model.
DISTRESSES CORRELATION ANALYSIS
To investigate the effect of one distress on the others correlation analysis among all distresses
was conducted. Table 2 shows the correlation matrix and probability values of the included distresses.
The analysis indicated that longitudinal and transverse cracks and weathering and raveling distresses
are highly correlated. That means one of these distresses leads to the existence of the other.
Weathering and raveling correlates highly with depression and potholes. The existence of cracks,
weathering and depressions on pavement surface indicate that potholes will appear in the same
pavement section. The high value of correlations among the different distresses is an indication that
existence of a distress on pavement surface is an inducement to other types of distress.
ANALYSIS OF DISTRESS BEHAVIOR
To investigate the propagation behavior of the common types of distress, the percent of each
distress density was plotted against time of survey for all severity levels. These plots are presented in
Figure 1 for main streets and in Figure 2 for secondary streets. It is clear that weathering and raveling
is the wide spread distress among all the other distresses for both main and secondary streets. In four
years period the percent of weathering and raveling density on main street increased by 100 %. This
represents about 80% of the main street sampled area. In six years period about 70% of secondary
street sampled area subjected to weathering and raveling. The percent density of weathering and
raveling increased on secondary streets by about 250% in six years. The percent density of
longitudinal and transverse cracks increased by about 175% in four years on main streets and by about
230% in six years for secondary streets. In four years, the percentage density of pothole increased by
about 300% on main streets and by about 400% on secondary streets in six years. Potholes covered
only 10% of the main streets sampled area in four years and 5% of secondary streets sampled area in
six years.
SUMMARY AND CONCLUSIONS
This paper investigates the behavior of the common types of pavement distress on Riyadh’s
street network. The analysis include determining the factors affecting distress density, correlation
analysis and propagation behavior of common pavement distresses. The street network was divided
into two main categories: main and secondary streets. The analysis utilizes data of three consecutive
distress surveys. These surveys covered a period of four years for main streets and six years of
secondary streets. Pavement sections that did not receive any maintenance during these periods were
only included in the analysis. The analysis involved 590 main street data points and 3119 secondary
street observations during each survey. The common main street distresses included in this study were:
longitudinal and transverse cracks, weathering and raveling, potholes and depressions. The secondary
street distresses included were longitudinal and transverse cracks, weathering and raveling and
potholes.
The analysis indicated that distress type, pavement condition, distress severity level, traffic level and
time all have a significant effect on density of individual distress in main streets and secondary streets
except severity level for potholes. Distresses correlation analysis indicated that high value of
correlations among the different distresses. This is an indication that existence of a distress on
pavement surface is an inducement to other types of distress. The distress behavior analysis proves
that each distress has different rate of propagation with time. While weathering and raveling is the
most spread distress, the percent of the distress density on main streets increased by 100 % in four
years and by 250% on secondary streets in six years. The percentage density of potholes is increased
by about 300% on main streets and represents only 10% of the sampled area in four years. In
secondary streets the increased reached 400% and about 5% of the sampled area is subjected to
potholes.
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REFERENCES
1. Haas, R., et. al., "Modern Pavement Management", Krieger Publishing Company,
Malabor, Florida, 1994.
2. Al- Swailmi, S., E. Sharaf, A. Al-Mansour, S. Zahran, M. Al-Mulhem and AL-Kharashi,
"Development of a Maintenance Management system For Riyadh Street Network", AR-
15-02, Final Report 1418H (1998).
3. Al- Swailmi, S., Al-Mansour, A., and et al “Developing Urban Distress Index for Riyadh
PMMS”, Fifth Saudi Engineering Conference, UmmAl-Qura University, 1999.
4. .AL-Mansour, A ., "Development of Pavement Performance Models for Riyadh Streets
Network" Transportation Research Board (TRB), No.1655, 1999.
5. Al-Mansour, A. and AL-Swailem, S. "Pavement Condition data Collection and Evaluation
of Riyadh Main Street Network", Journal of King Saud University, Engineering Science,
1997.
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Table 1. Analysis of Variance for Distress Density
P-value
Type of distress Factor Main
Street
Secondary
Street
Longitudinal and transverse
cracks
Pavement condition
Traffic level
Severity level
Time
.000
.000
.000
.001
.000
N/A
.006
.000
Pavement condition .001 .001
Utility patching
Traffic level
.000 N/A
Severity level
NA .003
Time
.002 .001
Pavement condition .001 .000
Weathering and Raveling
Traffic level
.000 N/A
Severity level
.001 .021
Time
.000 .000
Pavement condition .000 .002
Potholes
Traffic level
.624 N/A
Severity level
.005 .135
Time
.000 .001
Pavement condition .000 N/A
Depression
Traffic level
.076 N/A
Severity level
.062 N/A
Time
.000 N/A
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Table 2. Distresses correlation coefficient and probability matrix for main and
Secondary streets
Highway class Longitudinal and
transverse cracks
Longitudinal Main St. 1
and transverse
cracks Secondary St. 1
Weathering and
1
Main St.
Raveling
(0.013)
0.985
Secondary St.
(0.109)
0.999
Main St.
(0.024)
Potholes
0.998
Secondary St.
(0.043)
0.996
Main St.
(0.058)
Depression
Weathering
and Raveling
1
1
0.998
(0.037)
0.971
(0.153)
0.994
(0.072)
Potholes
1
1
0.999
(0.034)
Secondary St. N/A N/A N/A
Depression
1
N/A
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percent distress density
90
80
70
60
50
40
30
20
10
0
cracks
weathering
depression
potholes
0 2 4
Time (Years)
Figure 1. Distress propagation behavior on main streets
percent distress density
80
70
60
50
40
30
20
10
0
cracks
w eathering
potholes
0 4 6
Time (Years)
Figure 2. Distress propagation behavior on secondary streets
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