GIS-Based Framework for Prioritizing Road Maintenance and Repair
Salah Sadek*, Ramzi Ramadan*
Dept. of Civil and Environmental Engineering, American University of Beirut, Lebanon
Abstract:
The allocation of resources for road repair/maintenance is challenging given the number and na-
ture of the variables involved, which range from climate-related parameters to traffic and the geometries of
the road and associated structures. The need for an objective and adaptable decision support system is
clear. The work presented in this paper discusses the inception and implementation of such a framework
and tool, which integrate the strengths and capabilities of GIS. A dynamic tool was developed within
ArcGIS® which generates various road vulnerability and hazard maps with maintenance and/or repair priority zones, in reference to a number of specific climatological, topographical, geometric, geotechnical and
past performance criteria. Geo-statistical analyses can then be generated using a versatile interface. The
developed framework and methodology were tested in the Mount-Lebanon province, which is characterized
by a high variability in topography and rainfall precipitation regimes and a density and complex road network.
Landslide risk zones were generated in GIS, by cross-referencing and combining various data coverages
including a historic data record of actual road failures over three years. Based on the generated hazard
maps, an optimized maintenance scheme was proposed. The approach outlined in this paper could easily
be extended to cover any geographical location for which the necessary data layers are developed.
Key words:
GIS; Slope Stability; Hazard Maps; Road Maintenance; Road Repair.
Introduction
A number or researchers and practicing engineers have
explored the use of GIS in applications involving the
establishment of landslide hazard maps for both static and
seismic loading conditions [1], [2] and [3]. Sakellarious
et al. [4] presented a decision support system which
integrates GIS in evaluating slope stability. Their work
focused more on establishing characteristic parameters
for the soil layers and analysis algorithms for stability
calculations. In presenting an integrated GIS-based
framework for the siting or evaluation of potential
highway alignments, Sadek et al. [5] included as part of
the their assessment framework a slope stability
component embedded within the automated GIS-based
assessment.
The work described above, does not
incorporate a feedback scheme which involves actual data
from the areas of concern describing location, type and
extent of slope instability and more importantly their
potential impact on the surroundings and likely repair
costs. Such information would be very valuable in
building a “memory” to the process, which could then be
invested in producing better management decisions and
resource allocation priorities. The work described in this
paper is driven by those concerns and potential
applications.
1 Objectives and Scope
The objective of the work presented in this paper
was to establish a framework for developing a slope
stability hazard assessment tool, using state of the art
software and analysis technologies. A work methodology is developed which includes: data collection, failure/event classification and/or analysis, maintenance
prioritization, and future preventive measures.
The final outcome of the work presented herein was
the development of a decision-aid tool which allows
the relevant authorities and municipalities concerned
to:
 Assess the extent of particular landslide and slope
instability hazards and their implications (on safety, traffic, etc.).
 Optimize and prioritize the distribution of resources amongst the different affected areas.
 Eliminate the possibility of redundancy and duplication in the rehabilitation, reconstruction and
remedial works.
 Identify recurrent problem zones based on geographical and statistical analyses conducted on
data collected over the years.
 Anticipate/predict potential future hazards and
identify mitigation and preventive measures.
2
Decision Aid Framework
Yearly resources allocated by local and central
governments for road maintenance and repair are
limited and fixed. The task of prioritizing and optimizing maintenance and repair works is therefore a
real challenge, particularly in regions prone to recurring slope instability and road structure failures.
In adopting a platform for the proposed decision-aid
framework we opted for Geographic Information
System (GIS) applications. GIS presents an excellent environment for storing, displaying and maintaining all relevant data. GIS provides the capability
to perform the required spatial analyses and incorporate all factors that are mainly related to geography
including soil type, geology, topography, climatology and demography. Based on all the above, we have
developed a customized application for Road Damage Hazard Factor (RDHF) assessment, by creating
user friendly interfaces within the ArcGIS platform
(Figure-1).
Fig. 1 Developed user interface within ArcGIS for RDHF assessment
The custom-built application presents the user with
two interfaces: The first interface is designed for information data entry regarding a particular site and
prompts for and/or presents the following information:
 Site identification/reference: Nearest town or village and administrative sector (caza).
 Site characteristic properties: Universal coordinates, elevation (MSL), slope, soil type, geologic
setting and precipitation levels.
 Failure characteristics; type and assessment for
level of: Damage, current risk, cost and repair
complexity.
The above interface is used for data entry, data retrieving and updating. The second application interface
is designed and used for data analysis and processing,
as well as thematic mapping generation.
The proposed methodology can be divided into three
phases: Field data collection, office data processing,
and finally generating the road failure hazard maps,
maintenance prioritization, and future hazards assessment (Figure-2).
At the end of every significant or severe rainstorm,
the reported locations of road failures are obtained
from the concerned authorities and compiled. This
stage is followed by a comprehensive field data collection program which is implemented through site visits
to all relevant locations, where the following necessary
information is gathered:
 The “exact” geographic location using handheld
global positioning system (GPS).
 Photos of the general site area and local failure
characteristics and extent using a Digital Camera.
 Completion of a standard form or data sheet for
the site containing all necessary details and criteria including: Date, failure type/description, risk
level assessment (Table-1), damage level classification (Table-2), repair complexity and the associated cost.
.
DATA
SITE
- GPS Reading
- Digital Photos
- Form - Date
- Type Failure / Description
- Assessment
- Cost
- Current Risk Level
- Damage Level
- Repair Complexity


- Contours / Slope / Aspects
- Geology / Soil
- Land Cover / Use
- Faults
- Roads
- Rivers / Channels
- Rainfall (Yearly)
- Population
- Administrative Boundaries
OFFICE
- Data Entry
- Data Processing
- Analysis
- Queries Multi-Criteria
- Statistical Results
- Geo-Processing Analysis
- Comparison
Yearly
Historical
FAILURES / ASSESSMENT
- Current Situation
- Maintenance Prioritization
- Immediate Action
- Alternative Access Roads
- Rescue Plan
- Future Hazard Prediction
T
Fig.2 Schematic of the methodology and framework
3
Table 1. Risk level assessment – Proposed characterization chart.
Table-2 Damage level classifications and relevant work needed.
The customized GIS application and its database
management options are then used to store and maintain all the collected data, and to locate all the visited
sites on the base map. At this stage, the geo-processing
capabilities of GIS are used to overlay all the mapped
failure sites over the already compiled base map and
are automatically assigned additional attributes from
the following data layers: Topography: (typ. 10 meter
contour lines); Land cover / Land use; Geology / Soil
types; Fault Lines; Road network; Rivers / channels;
Average yearly rainfall; Administrative boundaries /
Population densities.
Given all the compiled data for the depicted sites, a
failure inventory map can be generated and multicriteria queries can be performed to produce the following: Maintenance priority plan (budgetary allocations); Rescue plans and alternative routings; Updated
hazard maps; Preventive and mitigation measures.
3 Examples from Application
In the following section, a sample output from a
case study encompassing all the described components
of the structured methodology detailed earlier is presented. It is based on the Mount Lebanon area which is
an administrative unit representing 19% of the total
Lebanon area with 24.7% of the total national road
network. The base ground elevations range from 0m to
2600m above sea level. The prevailing soil types vary
widely: agricultural soils, sandy clayey marls, limestones (C2), pale bedded limestone (C3-C5), basal
sandstones (C1), grey limestone (J4), local sandy limestone (J5-J7) as described in [6].
Hundreds of cases of road failures are typically reported every year. They occur in the winter season
(October to March) where total rainfall precipitation
over the period ranges from 600 to 1400 mm [7]. In
many if not all cases, repairs are implemented relatively quickly without any long term strategy or future
preventive considerations.
A comprehensive data collection campaign covering
the whole Mount Lebanon area was conducted during
years 2003 and 2004 as per the methodology described
earlier. The total number of mapped failures was 514
in 2003 and 164 in 2004. The recorded average precipitations were 1148 mm and 713 mm for the years
2003 and 2004 respectively.
The characteristic tools possible in a GIS environment allowed the compilation of the slope failure results in a number of interesting contexts. For instance,
if the slope/road failures are cross-referenced using the
soil/geology type data layers, slope instabilities appear
to occur mostly in sandy clayey marls/limestones. Furthermore, failure densities showed identifiable patterns, where failure clusters appear to occur within locations that are characterized by the following soil/land
covers geological features: Sandy top soils; Pine land
cover; or Near/on fault lines.
Maps showing high risk zones were generated by intersecting all data layers with the factors influencing
road/slope stability and comparing those with actual
failures reported. Such a map based on the computed
RDHF factors was produced for Mount-Lebanon post
the 2004 season and is presented in Figure-3.
Furthermore, high risk road sections and neighboring towns/villages can be identified on the generated
maps. Such information is very important in establishing emergency alternative routings and prioritizing resources in order to assure that access to remote villages
is not compromised and that efficient rescue planning
schemes in case of emergencies are developed.
References
[1] Zaitchik B.F. and van Es. H.M. (2003) “Applying a GIS
slope-stability model to site-specific landslide prevention in
Honduras”, Journal of Soil and Water Conservation, Vol.58,
No.1, pp. 45-53.
·
Legend
RDHF
High : 5
Low : 1
18,000
9,000
0
18,000
Meters
Fig.3- Hazard Map generated with highlighted risk zones
Conclusions
A framework and methodology for road failure risk
assessment and management was developed using a
GIS base reference. It was designed for road/highway
administrators and allows for more efficient road rehabilitation plans by prioritizing failure locations and
identifying possible correlations with causative factors.
The proposed methodology should also help to optimize preventive maintenance by identifying critical
zones and producing updated landslide and road damage hazard maps. The proposed tool is embedded fully
within ArcGIS and presents user friendly interfaces.
Sample output from the case study of the MountLebanon region was presented to illustrate some of the
capabilities of the tool and the associated possibilities.
mation Sciences, Vol.5, No. 2, pp. 121-128.
[4] Sakellariou M. G. and Ferentinou M. (2001) “GIS-Based
Estimation of Slope Stability”, Natural Hazard Review, Vol.
2, No. 1, pp. 12-21.
[5] Sadek S., Bedran M. and Kaysi I. (1999) “A GIS Platform
for Multi-Criteria Evaluation of Route Alignments”, Jour-
[2] Malkawi A., Saleh B., Al-Sheriadeh, M.S. and Hamza, M.S.
nal of Transportation Engineering. V. 125 No.2, pp.144-152.
(2000) “Mapping of Landslide Hazard Zones in Jordan Us-
[6] Dubertret, L. (1960) Carte Geologique au 50000 Feuilles de
ing Remote Sensing and GIS”, Journal of Urban Planning
and Development, Vol. 126, No. 1, pp 1-17.
[3] Khazai B. and Sitar N. (2000) "Assessment of Seismic
Slope Stability Using GIS Modeling," Geographic Infor-
Hermon. Institut Geographique National, Beirut, Lebanon.
[7] Lebanese Weather Observatory Department, General Directorate of Civil Aviation, Ministry of Public Works &
Transport. Precipitation Data (1950-2004).