See discussions, stats, and author profiles for this publication at: https://www.researchgate.

net/publication/282643833

Application of GIS as Support Tool for Pavement Maintenance Strategy

Selection

Conference Paper · June 2015

CITATIONS READS

0 1,070

5 authors, including:

Oluwafemi Adeleke Kolo Stephen

University of Ilorin Federal University of Technology Minna
16 PUBLICATIONS   21 CITATIONS    6 PUBLICATIONS   2 CITATIONS   

SEE PROFILE SEE PROFILE

Joseph Odumosu H S Abdulrahman

Federal University of Technology Minna 8 PUBLICATIONS   2 CITATIONS   
16 PUBLICATIONS   5 CITATIONS   
SEE PROFILE
SEE PROFILE

Some of the authors of this publication are also working on these related projects:

environmental capacity of heterogenous traffic on urban arterials View project

Traffic Engineering View project

All content following this page was uploaded by H S Abdulrahman on 07 October 2015.

The user has requested enhancement of the downloaded file.

 Application of GIS as Support Tool for Pavement Maintenance Strategy Selection
Adeleke O. O.1*, Kolo S. S.2, Odumosu J. O.3, Abdulrahman H. S.2 and Atilola B. Y1
1
Dept. of Civil Eng., Univ. of Ilorin, Ilorin, Nigeria
2
Dept. of Civil Eng., Federal Univ. of Tech., Minna, Nigeria
3
Dept. of Surveying and Geomatics, Federal Univ. of Tech., Minna, Nigeria

*Corresponding author: Tel. +234 8060812278 email: adeleke.oo@unilorin.edu.ng

ABSTRACT
Pavement Management System (PMS) is set of tools or methods that can assist decision
makers in finding cost effective strategies for providing, evaluating, and maintaining
pavement in a serviceable condition. The paper reports how ArcGIS software is used as a
decision support tool for the maintenance of road networks; the University of Ilorin paved
road network is taken as a case study. Pavement Surface Evaluation and Rating was
performed on each road in the network using pavement condition rating form and scale.
Spatial and aspatial information of the road network which include the digital map of the road
network, the coordinates of defect’s location, defect type and size etc. were used to develop a
relational database. The database developed in EXCEL software was imported into ArcGIS
software to allow for ease of analysis and query of the database and ease of visual and
graphical displays of results. The developed package which can easily be updated lends itself
to simple and multiple queries of the database such as ‘what is where and where is what’.
Results of queries on the pavement condition rating, the needed maintenance budget for the
roads and statistical analyses are given in the paper. It is recommended that relevant agencies
in the field of road maintenance should explore the use of GIS for the maintenance of
pavements and other roadway assets to enhance decision making process.

Keywords: Pavement condition, Pavement maintenance, GIS, Decision making

1
1. Introduction

Roads are the major channel of transportation for carrying goods, passengers and services.
The deterioration of these roads will fast affect the transportation system with consequent
adverse effects on the socio-economic activities of a nation; thus the responsibility for proper
maintenance and management of the road system by the supervising agencies. The major goal
of a highway agency is to use public funds to provide a comfortable, safe and economical
road surface. This requires balancing priorities and making difficult decisions in order to
manage the pavements effectively. Managing local roads involve three useful steps (i)
inventory of all roads and streets (ii) periodic evaluation of the condition of all pavements
and (iii) usage of the condition evaluations to set priorities for projects (Wisconsin
Transportation Information Centre, 2002). This demand on highway agencies has led to the
development of various Pavement Management Systems (PMS). A PMS is defined as a set of
tools or methods that can assist decision makers in finding cost effective strategies for
providing, evaluating, and maintaining pavement networks in a serviceable condition
(WNCHRP, 2004).

The conventional methods of locational referencing and Road Pavement management were
not suitable for comprehensive computerization of highway information (Lagunzad and
McPherson, 2003). Road information is geospatial and has recently being managed in
Geographic Information System (GIS) environment (Robert, 2011). GIS integrates hardware
to facilitate the management, analysis and graphical representation of all forms of
geospatially referenced data. It allows the user to interpret, question, track and visualize data
in ways that will establish trends, patterns and relationships, in the form of maps, reports and
charts. GIS helps answer questions and solve problems by looking at data in a way that is
quickly understood and easily shared to allow for better decision making. The infusion of
GIS has benefited the PMS development and implementation effort in these regards (Smadi,
2004). GIS for example, has been used in pavement management in roadway condition
assessment, maintenance strategies and improvement recommendations, prioritization of
roadway improvements, and development of preliminary cost estimate (Robert, 2011). This
realisation coupled with the need to enhance and improve on Pavement Management (PM) in
the study area informed the decision for the study which can serve as prototype for other PM
agencies.

2. Aim and objectives

The aim of the study is to develop a GIS-based PMS package which will provide a systematic
process for collecting, analyzing and summarizing pavement condition information to support
the selection and implementation of cost effective pavement maintenance programs. The
paved road network of the main campus of the University of Ilorin, Ilorin, Nigeria is used as
a case study. The objectives are as follows:
i. map out the road network of the Unilorin main campus
ii. evaluate the condition of the road pavement
iii. identify the appropriate maintenance and rehabilitation project needs on the road
iv. develop a GIS database of the spatial and attribute data of the road network and
v. Prioritize the allocation of financial resources based on outcomes of GIS analyses.

3. Literature review

2
3.1 Defects in flexible pavements
Defects in flexible pavements are indicative of road distress and impaired efficiency. They
may be due to poor performance of constituent materials, errors in design or construction,
environmental and climate factors as well as to particularly heavy traffic. Two types of
defects may be distinguished: functional and structural. Functional defects express shallow
degradation of the wearing course of pavements; this degradation reduces both vehicle grip to
the road and evenness of the road surface and thus jeopardizing traffic safety. Factors
responsible for this type of degradation and giving rise to skid resistance problems include
leveling (or polishing) of aggregates; surface exposure of bitumen (known as bitumen
blooming) and detachment of aggregates. Smoothness problems comprise longitudinal
undulations, transverse undulations (more commonly called ruts), hollows or bulges, dips on
extensive surfaces and edge cracking. Conversely, structural defects arise in the supporting
courses of the superstructure. They are due to deterioration of its load-bearing capacity and
have major repercussions on pavement durability. Defects of this type encompass surface
cracks and breaks and more recurrently, longitudinal, and transverse cracks, longitudinal
cracks only, transverse cracks only, ramified cracks (spider or alligator cracks) and failures (
Cologrande et. al., 2011; Wisconsin Transportation Information Centre, 2002).

3.2 GIS Applications

The use of Geographic Information System (GIS) in pavement management is utilized at
different levels and covers the different steps from developing to implementing a pavement
management system. GIS is used in the design of the PMS database, in the data integration
process (inventory, history, condition, etc) and finally in communicating the results of the
PMS (Smadi, 2004). Because the data used in the decision-making process in PMS have
spatial components, the use of spatial technologies such as GIS and GPS have been very
appealing. Georgia Department of Transportation (GDOT) for example has adopted and been
actively using GIS technology to improve its 28,962-km system's highway pavement
management since 2000 (Robert, 2011). The GDOT scheme includes an Oracle client/server
and a GIS-based pavement management module. Shanghai airport pavement system
(SHAPMS) has also been developed and updated for the Hongqiao and Pudong international
airports that also utilize the power of GIS for data collection, data storage, geospatial analysis
of pavement evaluation and optimization of maintenance planning (Meng, et al, 2012).

3.3 Global Positioning System

The use of Global Positioning System (GPS) in the data collection process (inventory of
condition) is a very useful tool in the pavement management development process and
implementation. Referencing all collected information geographically by capturing their
coordinates makes the data integration and representation as efficient as possible. (Smadi,
2004).

3.4 Pavement Condition Rating

Pavement surface defects can be distinguished and quantified visually by human or
automated techniques (Wisconsin Transportation Information Centre, 2002). Generally rating
procedures involve observing and recording the presence of specific defect type followed by
a description of its severity and then the quantification of its extent to which the road surface
is affected by the defect. Pavement Condition Rating (PCR) is a value from 0 to 100
computed for each road segment or project, where 100 indicates no defects. Where there are
defects the PCR is obtained by subtracting a ‘Deduct’ value from 100. ‘Deduct’ is a number
from 0 to 100 that is assigned to a combination of defects observed in a pavement segment. It
is obtained by assigning deduct value to each distress type and its levels of severity and

3
extent. The deduct values in a given segment or road are summed together and subtracted
from 100 to compute the final PCR for the segment or road.

4. Materials and methods

i. Road network
The study area and major adjoining features were mapped using Garmin GPS reciever, this
provided the base map.

ii. Pavement Evaluation and Rating

Pavement Surface Evaluation and Rating was performed on each of the roads using pavement
condition rating form and pavement condition rating scale. A visual conditional survey was
conducted by walking through the road lengths. Data collected during the exercise include:
 defect type
 geographic location of each defect (X,Y,Z coordinates) was captured with GPS
 the severity of defect,
 the extent to which the road surface is affected by the defect i.e measure of the area,
length or count associated with the defect

iii. Maintenance cost determination

This is the preparation of the financial implication of the failed sections of the roads. In order
to have an accurate maintenance cost analysis a well prepared proposal format on road
maintenance was used for the costing.

iv. Data processing and analyses

The collected data which includes the defect type, defect location, extent etc were input into
EXCEL, processed as appropriate and imported into ArcGIS Software (9.3 Version) for
further analyses and presentation of results.

5. Results and Discussion

5.1 Results
5.1.1 Road inventory
The roads in the network include: Senior quarters road, Unilorin stadium bypass, Unilorin
main road, Olu Daramola road, Afolabi Toye road and Abdullahi Mohammed road which
have a total length of 15.5 km. The lengths of the roads are shown in Table 1 while Fig. 1 is
the road network showing some adjacent structures.

Table 1: Road Length

Section Road name Road distance (km)
A Senior quarter`s road 3.553
B Unilorin stadium bypass 0.699
C Unilorin main road 5.191
D Olu Daramola road 1.486
E Afolabi Toye road 2.640
F Abdullahi Mohammed road 1.937

4
Fig. 1: The Road Network of Unilorin (Paved Section)
5.1.2 Identified defects
The defects identified on the roads include: Longitudinal crack, Transverse crack, Potholes,
Failed patches, Ravelling, Bleeding, Rutting, Depression, Edge break. Fig. 2 and Fig. 3 show
the positions of the defect types for road sections A, B and C and road sections D, E and F
respectively.

Fig. 2: The database of Sections A, B and C

5
Fig. 3: The database of Sections D, E and F

5.1.3 Pavement Condition Rating (PCR)

Pavement condition rating form and rating scale were used as guide in the study.
The PCR is obtained as
100 – TOTAL DEDUCT = PCR (pavement condition rating)
while
DISTRESS WEIGHT x SEVERITY WT x EXTENT WT = DEDUCT POINT

The pavement condition rating form for Section C is shown in Table 2 as an example: the
percentage of ravelling on the road which is below 20% gave 0.5 (EXTENT) from the PCR
form which is used to multiply standard 0.3 (L SEVERITY WT) and 10 (DISTRESS WT) to
obtain 1.5 (DEDUCT POINT). The obtained deduct point of each defect added together gave
the TOTAL DEDUCT which is subtracted from 100 to give the percentage condition of the
road.

The results of the pavement condition rating of Section A to Section F are shown in Table 3.

5.1.4 Database Development

An EXCEL file containing the defects coordinates, defect extent, cost etc was imported into
the ArcGIS using the “Add XY Data” tool. The data which was thus converted into shapefile
is shown as attributes in Fig. 4 and Fig. 5.

6
 Table 2: Pavement Condition Rating of Unilorin main road (Section C)

DISTRESS DISTRESS SEVERITY WT.* EXTENT WT.** DEDUCT

WEIGHT L M H O F E POINT***
RAVELING 10 0.3 0.6 1 0.5 0.8 1 10 X 0.3 X 0.5 =
1.5
BLEEDING 5 0.8 0.8 1 0.6 0.9 1 0

PATCHING 5 0.3 0.6 1 0.6 0.8 1 5 X 0.3 X 0.6 =

0.9
POTHOLES/ 10 0.4 0.7 1 0.5 0.8 1 10 X 0.7 X 0.8 =
DEBOUNDING 5.6
CRACK SEALING 5 1 1 1 0.5 0.8 1 0
DEFICIENCY
RUTTING 10 0.3 0.7 1 0.6 0.8 1 10 X 0.7 X 0.8 =
5.6
SETTLEMENT 10 0.5 0.7 1 0.5 0.8 1 0

CORRUGATION 5 0.4 0.8 1 0.5 0.8 1 0

WHEEL TRACK 15 0.4 0.7 1 0.5 0.7 1 15 X 0.4 X 0.5 =

CRACKING 3
BLOCK AND 10 0.4 0.7 1 0.5 0.7 1 10 X 0.4 X 0.5 =
TRANSVERSE 2
CRACKING
LONGITUDINAL JOINT 5 0.4 0.7 1 0.5 0.7 1 5 X 0.7 X 0.7 =
CRACKING 2.45
EDGE CRACKING 5 0.4 0.7 1 0.5 0.7 1 5 X 0.7 X 0.7 =
2.45
RANDOM CRACKING 5 0.4 0.7 1 0.5 0.7¤ 1 0

*L= LOW **O= OCCASIONAL TOTAL DEDUCT = 23.5

M= MEDIUM F= FREQUENT

H= HIGH E= EXTENSIVE 100 – TOTAL DEDUCT = PCR = 76.5

***DISTRESS WT x (SEVERITY WT) x (EXTENT WT) = DEDUCT POINT

Table 3: PCR Results

Section A Section B Section C Section D Section E Section F
68.4 82.5 76.5 86.5 93.0 98.0
Fair Good Fair Good V Good V Good

7
Fig. 4: Defects data

Fig. 5: Data on features along the roads

5.1.5 Cost Analysis

This involves the importation of the cost data into the GIS software to prepare the budget for
each section of the road network as shown in Fig. 6. The costing was obtained as follows:

i. Compute the defect volume by: Defect area x Average depth

ii. Compute tonnage of asphalt required by: Volume of defect x 2.4
iii. Compute total cost of asphalt by: Asphalt tonnage x rate per ton
iv. Compute cost of workmanship by: Defect area x Maintenance rate per m2
v. Compute total cost of maintenance: Total cost of material + Total cost of workmanship

8
Fig. 6: Cost analysis for the road network

5.1.6 Database Query

The main objective of pavement condition rating is to serve as a support tool in planning
budgets and priorities. The database was queried to attain this purpose. The queries include
both simple and multiple queries.

5.1.6.1 Query on the pavement condition rating

The pavement condition of each section of the road network was queried by the GIS and the
result is shown in Fig. 7.

Fig. 7: Pavement condition rating of the road network

5.1.6.2 Query on road budget

The statistical analysis of the budget for each road is shown in Fig. 8 while the graphical
display is shown in Fig. 9.

9
Fig. 8: Statistical analysis for the budget

Fig. 9: Budget for each road

5.1.6.2 Simple Query

The database was queried to indicate the roads with budget greater than N 264,960; the result
shows the Senior quarter’s road and Unilorin main road as seen in Fig. 10.

5.1.6.3 Multiple Queries

The database query to indicate Roads not longer than 1.486km and with Budget less than N
264,960 gave the result as Afolabi Toye road and Abdullahi Mohammed road as shown in
Fig. 11.

10
Fig. 10: Simple query of Roads with Budget greater than N 264,960

Fig. 11: Multiple queries of Roads not longer than 1.486km with Budget less than N 264,960

5.2 Discussion
The results obtained from the analysis of the collected data and the pavement condition
rating, gave the level of maintenance required in ascending order as Section A, Section C,
Section B, Section D, Section E and Section F. The prepared budgets of each section clearly
showed the financial implication of implementing maintenance program. The use of GIS in
the management of pavement condition rating data allows for several analyses, queries and
both visual and graphical display of information which lend their support to decision making.

11
Through color-coding of pavement defects, the road network condition were displayed which
can be viewed and projected for work programs. It displayed and reported the tables and
charts showing final information that are necessary for taking cost-effective decision on
maintenance strategies and management of the paved roads. Fig. 9 for example easily shows
the relative amount needed for the maintenance of each road and can help in prioritising
projects. In the case of a large road network such simple and multiple queries as
demonstrated in Section 5.1.6.2 and Section 5.1.6.3 can be used for example to assess the
locations or geographical spread of the roads that can be maintained within a budget thereby
assisting decision makers on maintenance priorities in situation where geographic spread may
be a factor in decision making.

6. Conclusion and Recommendation

GIS has been successfully used in the management of pavement condition rating data which
is geospatial. The GIS capacity to carry out analyses on the geospatial pavement condition
rating data and give visual and graphical display of the results has been demonstrated in the
study. GIS thus can be used to build up PMS which will certainly with its attributes serve as a
support tool for pavement maintenance strategy selection. Based on this study it is
recommended that road maintenance agencies should explore the use of GIS as support tool
for pavement management and similar geospatial assets due to its versatility in data analyses,
query capability as well as visual and graphical representation.

REFERENCES

Cologrande S., Ranalli D. and Tallini M. (2011). “Ground Penetrating Radar Assessment of
Flexible Road Pavement Degradation”, International Journal of Geophysics Vol. 2011,
Article ID 989136.

Luz, V. Lagunzad and Kevin McPherson (2003). GIS applications for road network of the
Philippines: a new technology in road management. Journal of Eastern Asia Society for
Transportation Studies. Vol 5, Oct. 2003. Pp 841 – 854.
Meng L, Yuan J. and Wenial C. (2012). ”Application of GIS in Shanghai Airport Pavement
Management System”. Shanghai Airport Authority, Shanghai 200335, P.R. China.

Robert K. (2011). “GIS Based Pavement Maintenance: A Systemic Approach”. College of

Technology Directed Projects. Paper 36, Purdue University, West Lafayette, Indiana, USA.

Smadi O. G. (2004). “Quantifying the Benefits of Pavement Management”, 6th International

Conference on Managing Pavements, the Lessons, the Challenges, the Way Ahead. Brisbane,
Queensland, Australia.

Wisconsin Transportation Information Centre (2002): Asphalt PASER Manual,

Transportation Information Centre, University of Wisconsin–Madison, USA

WNCHRP (2004). Web Document 35 (Project C1-38): Contractor’s Final Report

Washington, D.C, USA.

12
 WSDOT (1999). “Washington State Highway Pavements: Trends Conditions and Strategic
Plan”. WSDOT Field Operation Support Service Center, Materials Laboratory. Olympia,
USA.

13

View publication stats