prediction of road pavement damage for local
TRANSCRIPT
PREDICTION OF ROAD PAVEMENT DAMAGE FOR LOCAL ROADS IN
MALAYSIA
NASRADEEN ALI KHALIFA MILAD
Thesis submitted in fulfillment of the requirements
For the award of the degree of
Doctor of Philosophy
Faculty of Engineering Technology
UNIVERSITI MALAYSIA PAHANG
JUNE 2016
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ABSTRACT
The high wheel loads of heavy trucks are a major source of pavement damage by
causing fatigue, which leads to cracking and permanent deformation, which produces
rutting. Malaysia, as one of the developing country has high level of road pavement
damage. In addition to the cost of rehabilitating the pavement, serious safety issues
occurs especially when the heavy trucks using U2/U3 roads, which not design to be use
by heavy trucks. With aim to develop an analysis method and corresponding tool for
local authorities to evaluate the impact of heavy trucks on local access roads, an
observation was carried out to determine the characteristics of the trucks and operating
conditions on local roads, from February, 2013 until July, 2014 at the Taman Kosas
Utama Ampang Selangor and Taman Tas Kuantan Pahang (Malaysia). The mechanics
of truck movement on the local access roads were studied to identify relationships
between truck properties and road damage and to develop an appropriate method of data
collection for these local roads. The WarpPLS is used as tool to develop the method and
SPSS is used to examine the data and generate the model. Results indicated that
regression relationships between road damage and other research factors been
established with a coefficient of determination (R) at value of 0.71.
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ABSTRAK
Muatan berlebihan bagi trak muatan berat merupakan penyebab utama kerosakan jalan.
Muatan berlebihan mengakibatkan struktur jalan menjadi lemah dan membentuk
rekahan yang menyebabkan kerosakan serta pembentukan alur. Malaysia sebagai
sebuah negara membangun mempunyai kadar kerosakkan jalan yang tinggi. Selain dari
pertambahan kos pembaikan jalan, isu keselamatan juga menjadi perkara utama kerana
jalan jenis U2/U3 tidak direka bagi kegunaan trak muatan berat. Pemantauan terhadap
trak muatan berat yang menggunakan jalan-jalan yang telah dipilih dilakukan dari bulan
Febuari 2013 hingga Julai 2014 Taman Kosas Utama Ampang Selangor dan Taman Tas
Kuantan Pahang (Malaysia). Ini bertujuan mengenalpasti cara analisis kajian dan alatan
yang bersesuai untuk kegunaan pihak berkuasa tempatan bagi menilai kesan
penggunaan jalan-jalan tersebut oleh trak muatan berat. Pemantauan juga dilakukan
bagi mendapatkan ciri-ciri trak muatan berat dan keadaan semasa jalan-jalan tersebut.
Kajian berkaitan pergerakan mekanikal trak pada jalan-jalan ini juga dilakukan bagi
mengenalpasti hubungan antara ciri-ciri trak dan kerosakan jalan yang terjadi. Ini
penting bagi membangunkan cara pengumpulan data yang bersesuaian dengan objek
kajian. WarpPLS digunakan bagi pembangunan cara kajian dan SPSS digunakan bagi
menghasilkan model kajian. Hasil kajian mendapati terdapat hubungan regresi antara
kerosakkan jalan raya dan faktor-faktor yang dikaji dengan koefisien determinasi
dengan nilai 0.71.
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TABLE OF CONTENTS
DECLARATION Page
TITLE PAGE i
ACKNOWLEDGEMENTS ii
ABSTRACT iii
ABSTRAK iv
TABLE OF CONTENTS v
LIST OF TABLES xi
LIST OF FIGURES xii
LIST OF ABBREVIATIONS xv
CHAPTER 1 INTRODUCTION 1
1.1 Background 1
1.2 Problem Statement 3
1.3 Research Question 4
1.4 Research Objectives 5
1.5 Scope Of Research 5
1.6 Limitations Of This Study 5
1.7 Research Contributions 6
1.8 Significance Of Research 6
1.9 Variables Involved 6
1.10 Layout Of The Thesis 7
CHAPTER 2 LITERATURE REVIEW 8
2.1 Introduction 8
2.2 Road Network In Malaysia 8
2.2.1 Federal roads 8
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2.2.2 State Roads 8
2.3 Road Standards in Malaysia 8
2.3.1 Standardization 8
2.3.2 Application of road standards 9
2.4 Pavement Design Methodology 11
2.5 Characteristics Of Data Sources 14
2.6 Modelling Approaches 16
2.6.1 Software Used for Analysis in this research 17
2.7 Review Of Prediction Models 19
2.7.1 AASHTO Model 19
2.7.2 Kenya Road Transport Cost Study Model 20
2.7.3 Brazil Models 20
2.7.4 Texas Flexible Pavement Design System Model 21
2.7.5 Damage Index Model 21
2.7.6 HDM – III Models 22
2.7.7 Mississippi PCR Model 23
2.7.8 Arizona DOT Models 23
2.7.9 State of Washington Model 23
2.7.10 CRRI Models 24
2.7.11 CRS Model for Illinois Interstate Highway System 24
2.7.12 HDM - 4 Models 25
2.8 Road Pavement Structure 25
2.9 Traffic Loads 27
2.10 Heavy Vehicle 30
2.11 Road Pavement Damage 31
2.12 Types Of Damages 34
2.13 Characteristics Effecting Failure Of Pavement 36
2.13.1 Truck factors 36
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2.14 Determination Of Design Traffic 38
2.15 Asphalt Pavement Structure 41
2.16 Analytical Pavement Design 41
2.17 Pavement Management System 42
2.18 Pavement Types And Failure Modes 43
2.19 The Need To Predict Deterioration 44
2.20 Traffic Loading Effect 45
2.21 AASHTO Road Test 50
2.22 Fundamental Equations 51
2.23 Summary 54
CHAPTER 3 METHODOLOGY 57
3.1 Introduction 57
3.2 Desktop Study 59
3.3 Site Selection Criteria 61
3.4 Preliminary Site Observation 61
3.5 Sampling Technique 61
3.6 Sample Size 62
3.7 Data Collection 63
3.8 Statistical Analysis 63
3.9 Study Approach 66
3.10 Research Design 66
3.11 Observation Method 67
3.12 Documenting Observations: Field Notes 68
3.13 Scale Measurement Method 69
3.14 Theoretical Framework And Model 70
3.15 Approach Of Testing For Moderating Variables 71
3.16 The Perceived Behavioral Control 72
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3.17 Research Model 73
3.18 Analysis 73
3.19 Research Hypothesis 74
3.20 Summary 74
CHAPTER 4 DATA ANALYSIS, FINDINGS AND 75
4.1 Introduction 75
4.2 Assessing Research Objective 1 75
4.2.1 Frequency Analysis of Duration of Following 76
4.2.2 Frequency Analysis of Number of Roads 77
4.2.3 Frequency Analysis Of Frequency 78
4.2.4 Frequency Analysis of The Type Of Access 81
4.2.5 Frequency Analysis of Number Of Trucks 81
4.2.6 Frequency Analysis of Road Damage 82
4.2.7 Frequency Analysis of Number of Alxe 83
4.3 Crosstabulation analysis 85
4.3.1 Crosstabulation Duration of Following VS Road Damage 85
4.3.2 Crosstabulation Duration of Following VS Number of Axle 86
4.3.3 Crosstabulation Number of Road VS Road Damage 86
4.3.4 Crosstabulation Number of Road VS Number of Axle 87
4.3.5 Crosstabulation for Frequency VS Road Damage 88
4.3.6 Crosstabulation for Frequency VS Number of Axle 89
4.3.7 Creosstabulation for Type of Access VS Road Damage 89
4.3.8 Creosstabulation for Type of Access VS Number of Axle 90
4.3.9 Creosstabulation for Number of Trucks VS Road Damage 90
4.3.10 Creosstabulation for Number of Trucks VS Number of Axle 91
4.4 Spearman’s Correlations 92
4.5 The Case Processing Summary 93
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4.6 Model Fitting Information 94
4.7 Goodness of Fit 94
4.8 Pseudo R-square 95
4.9 Likelihood Ratio Tests 96
4.10 Parameter Estimates 96
4.11 Comparing Accuracy Rates 100
4.13 Discussion 100
CHAPTER 5 MODEL DEVELOPMENT AND VALIDATION 103
5.1 Introduction 103
5.2 Assessing Research Objective 2 103
5.3 Reliability And Validity 104
5.4 General Result 106
5.5 Assessment Of Proposed Hypotheses 106
5.6 A Fundamental Model Of Road Damage Measurements 107
5.6.1 Regression Equation of Road Damage Model 108
5.7 An Empirical Model Of Road Damage 109
5.8 Model Validation 110
5.9 Area of Model Validation 110
5.10 Sample Size for The Model Validation 111
5.11 Data Collection 112
5.12 Model Validation Progress 112
5.13 General Result 113
5.14 Models Comparisons 115
5.15 Discussion 116
CHAPTER 6 CONCLUSION AND RECOMMENDATION 118
6.1 Introduction 118
6.2 Conclusion 118
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6.3 Recommendation and Future Research 121
6.4 Value of The Study 122
6.6 Summary 123
REFERENCES 125
APPENDIX A 138
APPENDIX B 141
APPENDIX C 161
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LIST OF TABLES
Table Title Page
2.1 Shown each road design standard 10
2.2 Urban area JKR road design with more details 11
2.3 Lists of Research Projects on Pavement Damage 31
2.4 Surface deformation 35
2.5 Surface defect 35
2.6 Cracking crack 36
2.7 Patching and Potholes 36
2.8 Comparison of Axle Load among Various Countries 37
2.9 Summary of characteristics influencing pavement damage 38
2.10 Axle Configuration and Vehicle Load Factors based on Traffic
Categories used by HPU 39
3.1 Show the selected JKR U2/U3 roads in details for inspection/observation 60
3.2 Variables used in the study 76
4.1 Frequency of duration of following 76
4.2 Frequency of a number of roads 78
4.3 Frequency of residential road usage frequency 79
4.4 Frequency of type of access 80
4.5 Frequency of a number of trucks 81
4.6 Frequency of road damage 83
4.7 Frequency of number of Axle 84
4.8 Duration of following VS Road Damage Crosstabulation 85
4.9 Duration of following VS Number of Axle Crosstabulation 86
4.10 Number of the road VS Road Damage Crosstabulation 87
4.11 Number of the road VS Number of Axle Crosstabulation 88
4.12 Frequency VS Road Damage Crosstabulation 88
4.13 Frequency VS Number of Axle Crosstabulation 89
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4.14 Type of access VS Road Damage Crosstabulation 90
4.15 Type of access VS Number of Axle Crosstabulation 90
4.16 Number of the trucks VS Road Damage Crosstabulation 91
4.17 Number of the trucks VS Number of Axle Crosstabulation 92
4.18 Spearman’s correlations 93
4.19 Case Processing Summary 93
4.20 Model Fitting Information 94
4.21 Goodness of Fit 95
4.22 Pseudo R-Square 95
4.23 Likelihood Ratio Tests 96
4.24 Parameter Estimates 97
4.25 Classification 100
5.1 Alpha coefficients, composite reliability, VIFs, AVE 105
5.2 Model evaluation overall fit measurement 106
5.3 The significant structural relationships among the research variables 107
5.4 The equation model needs (Coefficientsa) 108
5.5 Show the selected JKR U2/U3 roads in details for inspection/observation
for validation 111
5.6 Alpha coefficients, composite reliability, VIFs, AVE 114
5.7 Model validation overall fit measurement 115
5.8 Model general comparisons 116
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LIST OF FIGURES
Figure Title Page
2.1 Flow chart for the selection of the, required design standard 12
2.2 Road damage at Hulu Selangor and Gombak 14
2.3 General road pavement structure 27
2.4 Load associated stresses in the Pavement structure 28
2.5 Stresses with respect to time 28
2.6 Stress distribution in the Pavement structure 28
2.7 Stresses at the Crack Tip in Overlays Due to Traffic 30
2.8 UK asphalt pavement structure 41
2.9 Major component of the Pavement system 43
2.10 Cross-Section of Pavement 44
2.11 Near Surface Pavement Stresses Induced by Tire Loading 48
2.12 Effect of bond on crack initiation and propagation 49
2.13 The Fourth Power Relationship between Axle Weight and Pavement
Damage 50
2.14 AASTHTO Flexible Pavement Design Nomograph. 51
2.15 Real road hierarchy 52
2.16 Conflict road hierarchy 52
2.17 Self-contained zones with separate access and mobility purposes 52
2.18 Pavement condition index (PCI), Rating Scale, and Suggested Colors 53
3.1 Flowchart Illustrating the Research Methodology 58
3.2 shows the Access to the Ampang area from both MMR2 and Elevated
Highway 59
3.3 Residential Area and other Facilities for the Study Area 60
3.4 Model Development Process. 66
3.5 Theory of Planned Behavior 72
3.6 the research model 73
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4.1 Histogram of duration of following variable 77
4.2 Histogram of a Number of road 78
4.3 Histogram of the frequency 79
4.4 Histogram of type of access 80
4.5 Histogram of a Number of trucks 82
4.6 Histogram of Road Damage 83
4.7 Histogram of Number of Axle 84
5.1 Fundamental Model of Road Damage Measurement 108
5.2 Empirical Model 109
5.3 study area for validation 111
5.4 The process of the model Validation development 113
5.5 Fundamental Model of Road Damage Measurement Validation 114
5.6 Self Study General Comparisons 110
6.1 Empirical Models for Road Damage 121
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LIST OF ABBREVIATIONS
PMLOA Pan Malaysia Lorry Owners Association
AASHTTO American Association of State Highway and Transportation Officials
JKR Jabatan Kerja Raya
ESAL Equivalent Standard Axle Load
FHWA Federal Highway Administration
HPU Highway Planning Unit
MAL Maximum Axle Loads
MGVM Permissible Gross Vehicle Weights
VLF Vehicle Load Factors
PMS Pavement Management System
HDM Highway Development and Management
ANN Artificial Neural Network
OLS Ordinary Least Squares
PWD Public Working Department
PERS Performance and Economic Rating System
SMP Strategic Management Plan
CHAPTER 1
INTRODUCTION
1.1 BACKGROUND
Malaysia is moving towards becoming a developed country. Roads play an
important part in the trade and transportation planning throughout the cosmos, and it has
became rapidly increased in the pavement infrastructure development in Malaysia,
mostly road linking the state capitals, airports, railroad stations and ports, gazetted
under Federal Roads Ordinance. Presently, Malaysia has more than 80,300 km roads,
and the roads are divided into three primary classes, namely toll expressway 1,700
(km), federal roads 17,500 (km) and state roads 61,100 (km) and the life spans are
between 10 to 15 years (Zakaria, 2005). Local authority road (city mall, municipal or
local council) or village (district office) street depend on the areas and naturally kept up
by the responsible local authority (Haron, 2004).
Yearly, the increases of heavy vehicles due to the high demand from industries,
subjected the pavements to heavy axle loads. Yet, conflicting to the prevailing literature
and the other road users’ speculations, the Pan Malaysia Lorry Owners Association
(PMLOA) objects to the fact that its trucks bearing any danger to other road users or
damage to the (Golias I., 2001).
The heavy vehicle load on the pavement subjects it to high stresses causing
damage. However, not all tracks have the same harmful effects; the damage to the road
pavement depends on wheel loads, number and location of axles, load distributions,
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number of wheels, tire types, inflation pressure and other factors (Gillespie T.D., 1993).
Heavy truckloads are the major cause of pavement damage. The size and configuration
of vehicular loads together with the environment have an important impact on induced
tensile stresses within flexible pavement (Yu H.T., 1998).
The length of the bulge front/rear, Height of vehicles, impacts on the increasing
carrying capacity of vehicles. Furthermore, this will immediately increase load axis of
the vehicle making the axle load greater than is permitted. This produces the problem of
excessive load or overloading. The impact of overload conditions on the road pavement
is premature failure, that is, a condition that the damage can shorten the lifespan of
roads before the design lifetime of the road is reached, (Badan Litbang Kementerian
Pekerjaan Umum, 2004).
Wheel load on the pavement results in distribution and reduction of stresses
throughout the Pavement structure, specifically for flexible pavement, stresses induced
in a pavement structure by traffic loads are highest in the upper layers and diminishes
with depth. A stress acting on an elemental cube of the pavement system experience
normal and shear stresses along the opposite sides of the block, as the paving material is
subjected to traffic load, the principal stresses are independent of the chosen coordinate
system. When a pavement structure is subjected to loads induced by traffic wheel, it is
classed as a dynamic load. The resultant stress consists of horizontal, vertical and shear
component. These stresses are routinely changed as the wheel load pass (Lekarp, 1999).
This improves a comprehensive theoretical framing of driver performance, stating
the relations amongst the dissimilar factors of driving performance. Several sources of
information must be used in order to estimate models based on the framework. This
study uses the observation method to gather information. Heavy vehicles also led to the
Damage of the road; there were 19.3 million registered vehicles on the Malaysia’s road,
and the government spent RM5 billion between 2001 and 2010 to continue its
sustenance on all the Federal roads (Kordi et al., 2010).
Damages on the roads are mainly caused by the heavier axle loads associated with
large commercial trucks (Croney, 1997). For this cause, every state has legislated
maximum axle load limit and maximum gross vehicle weight to be followed (Rezqallah,
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1997). The repetition of load and overloading of heavy trucks allegedly affect the road
pavements; the design life of the pavements becomes shorter, although the same quality
standard is used during design and construction (Mulyono, 2010), as much research
confirms that the Roads are damaged by heavy trucks, and every researcher has
implements different method from another to reach this conclusion. Although most of
those methods are time consuming, costly, unsecure, can’t be done individually; none of
them had confirmed when and how these roads got damaged. This thesis will present a
sample method to measure the road damage and to confirm when and how these roads
got damaged.
The causes of pavement failures are separated into two types, which are an internal
and external failure. Internal failures of pavement are frequent because of lack of
pavement mixture, weaknesses of component materials and poor construction. In the
meantime, external failures are as a result of overloading, diesel spillage, flooding,
sinkholes and other unforeseen reason such as earthquake, volcanoes and others. The
failures of flexible pavement are separated into four categories, which are surface
deformation, surface defects, cracking and patching and potholes (AASHTO, 2001).
1.2 PROBLEM STATEMENT
Statistics is the mathematical science involving the data collection, analysis and
interpretation of data. A number of specialties have evolved to apply statistical theory
and methods to various disciplines. Certain topics have "statistical" in their name but
relate to manipulations of probability distributions rather than to statistical analysis. To
think statistically is to know that the measurements taken in an experiment are subject
to systematic and random sources of variation, and that it is beneficial to base methods
of data analysis on probabilistic models. For many types of data analysis problems,
there are no more than a couple of general approaches to be considered on the route to
the problem's solution. Within the different approaches for a specific problem type,
there are usually at most a few competing statistical tools that can be used to obtain an
appropriate solution. The most type of data analysis problems is that selection of the
best statistical method to solve the problem is largely determined by the goal of the
analysis and the nature of the data. Model building, however, is different from most
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other areas of statistics with regard to method selection. There are more general
approaches and more competing techniques available for model building than for most
other types of problems. There is often more than one statistical tool that can be
effectively applied to a given modeling application. The large menu of methods
applicable to modeling problems means that there is both more opportunity for effective
and efficient solutions and more potential to spend time doing different analyses,
comparing different solutions and mastering the use of different tools. The remainder of
this section will introduce and briefly discuss some of the most popular and well
established statistical techniques that are useful for different model building situations.
Linear least squares regression is by far the most widely used modeling method. It is
what most people mean when they say that they have used "regression", "linear
regression" or "least squares" to fit a model to their data. Not only is linear least squares
regression the most widely used modeling method, but it has been adapted to a broad
range of situations that are outside its direct scope. It plays a strong underlying role in
many other modeling methods, including the other methods like nonlinear least squares
regression extends linear least squares regression for use with a much larger and more
general class of functions. Almost any function that can be written in closed form can
also be incorporated in a nonlinear regression model. Unlike linear regression, there are
very few limitations on the way parameters used in the functional part of a nonlinear
regression model. The biggest advantage of nonlinear least squares regression over
many other techniques is the broad range of functions that can be fit. Although many
scientific and engineering processes can be described well using linear models, or other
relatively simple types of models, there are many other processes that are inherently
nonlinear. For example, the strengthening of concrete as it cures is a nonlinear process.
Research on concrete strength shows that the strength increases quickly at first and then
levels off, or approaches an asymptote in mathematical terms, over time. Linear models
do not describe processes that asymptote very well because for all linear functions, the
function value can't increase or decrease at a declining rate as the explanatory variables
go to the extremes. There are many types of nonlinear models that well describe the
asymptotic behavior of a process. Like the asymptotic behavior of some processes,
other features of physical processes can often be expressed more easily using nonlinear
models than with simpler model types.
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1.3 RESEARCH QUESTION:
1. Is road pavement damage on the access road to the residential area caused by the
presence of unwanted heavy vehicles? 2. Are there any specific times and days that heavy vehicles frequently used these
roads? 3. What is the type of heavy truck (Number of Axle) that caused damage to the road
pavement? 4. Is the frequency and the volume of heavy vehicle using the road related to the
severity of the road damage?
1.4 RESEARCH OBJECTIVES
1. To investigate and analyze the effect of different factors on the pavement damage
of local roads at selected site; and
2. To develop and validate statistical relationship between different factors on the
pavement damage of local roads in selected site;
1.5 SCOPE OF RESEARCH
The study was carried out in a residential area that has a JKR U2/U3 road network
with high volume of heavy vehicle. This research was conducted from an engineer’s
perspective dealing with mathematical models and statistical methods. However, the
most important contribution of this research study is in statistics.
1.6 LIMITATIONS OF THIS STUDY
The low corporation rate experienced in this study was a concern. Follow-up
observation utilized to encourage responses. These observation follow-ups did
lead to a higher response rate than that which was initially received. Even so, a
higher response rate would potentially have led to more statistically significant
results.
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The collected statistics were limited to a certain chamber of the study area. The
reason for selecting areas within this chamber was to make them readily available
in the Taman Kosas Utama 1 area.
This study was undertaken by a first-time researcher, which might also be
seen as a limiting factor.
1.7 RESEARCH CONTRIBUTIONS
The primary contribution of this research is not the growth of a Damage model,
rather the demonstration of the feasibility of using joint estimation and its many
advantages, such as: 1. Identification and quantification of new variables,
2. How the road damage data should be collected/arrange
3. Efficient parameter estimates,
4. Identification of the JKR U2/U3 Roads Damage Index when every single variable
involved,
5. The statistics method to be used for Nonparametric Data.
6. The use of WarpPLS as tool to develop the research model
1.8 SIGNIFICANCE OF RESEARCH
It is expected that the models created in the course of this research will help the
local authority to monitor issues related to road Damage in residential areas (JKR
U2/U3 Roads). Specifically the study will help improve original designs on the road,
suggest better material types, advice quality construction, determine the threshold of
traffic volume and axle loading, examine the road geometry and pavement age. Also,
the study will contribute to results positively, and they are better environmental
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conditions and maintenance policy towards roads.
1.9 VARIABLES INVOLVED 1. Road Damage: -this variable is the Dependent Variable, and the road damage
explains the Damage caused by the heavy trucks with different types of damage to the
Road. 2. Duration of Following: - this variable is independent variable, and this variable
will check from where the truck come and to where it is heading by going through a
residential area and writing down the time that the observer spend to follow this truck. 3. Frequency: - this variable is independent variable, and this variable will check if
the same truck has gone through the residential area (study area) frequently. 4. Type of Access:- this variable is independent variable, and this variable will
check if the truck has gone through the residential area (study area) for the purpose of
passing through or trip end. 5. Number of Road: - this variable is independent variable, and this variable will
check from where the truck comes and to where it goes through a residential area and to
count how many roads that the driver used until reached the residential area.
6. Number of Trucks:- this variable is independent variable, and this variable will
allow the observer to count how many trucks were at residential area (study area), as an
observer and everyone know that the road damage comes from the heavy truck, but not
in a residential area, where the road design not to carry those types of trucks. 7. Number of Axle: - this variable is independent variable, and this variable will
allow the observer to check the type the trucks found in the residential area (study area),
by looking weather these trucks are one axle, two axle three axle four axles and so on.
1.10 LAYOUT OF THE THESIS
Chapter 1 Provides the Background to the research, including problem
Statements, objectives of the study, the scope, hypothesis of research, limitations of
the study.
Chapter 2 Contains the comprehensive review on the literature review in this
research field, offering definitions of positions from the perspectives of several
researchers.
Chapter 3 Presents the detailed description of the research methodology for the
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site selection, data collection.
Chapter 4 Explains the result and discussion as well as the statistical method
employed in objective 1.
Chapter 5 Explains the result and discussion as well as the statistical method
employed in objective 2.
Chapter 6 Concludes the study with some recommendation for future research.
CHAPTER 2
LITERATURE REVIEW
2.1 INTRODUCTION
This chapter highlights the road network in Malaysia and road standards and the
Application of road standards. This is followed by discussion of the data characteristics
that need to be considered for developing deterioration models. The empirical and
mechanistic approaches to model development are briefly discussed, and their
advantages and disadvantages are highlighted. Finally, this chapter reviews the existing
deterioration models with focus on explanatory variables used and the conditions under
which the models were developed.
2.2 ROAD NETWORK IN MALAYSIA
2.2.1 Federal Roads
Federal roads are all roads declared under the Federal Roads Ordinance (1959)
and the major interurban roads joining the state capitals and roads leading to points of
entry to and exit from the country.
2.2.2 State Roads
State roads comprise of the primary roads providing intrastate travel between the
district administrative centers. Other roads included in this class are the urban collector
roads under the municipalities and other minor roads within the hamlets and the rural
inhabited areas under the Districts Offices.
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2.3 ROAD STANDARDS IN MALAYSIA
2.3.1 Standardization
According to JKR manual, the geometric design of all roads needs to be
standardized for the following reasons:
a) To maintain stability in the design of roads according to their performance
demands.
b) To offer a consistent, dependable and reliable road facilities for the movement
of traffic;
c) To provide a guide for less subjective decisions on road design;
According to JKR manual, Rural and Urban Areas the Urban areas are defined as
areas having a population of at least 1,000 where buildings and houses gathered, and
business activity is prevalent. It covers all areas within the gazette Municipality limits
and also includes areas expected to become urbanized within the program period. Rural
areas can be regarded as areas other than urban areas (Arahan Teknik (Jalan) 5/85,
1985).
There is no essential difference in the ideologies of design for rural and urban
roads. Roads in urban regions are considered busy due to the pedestrian activities and
frequent stopping of vehicles owing to short intersection spacing’s and congested built-
up areas. Lower design speeds usually adopted for urban roads, and different cross-
sectional elements are applied with reference to the nature of traffic and adjoining land
use. It is for these reasons that variations in certain views of geometric design
incorporated for these two broad groups of roads.
2.3.2 Application of road standards
According to JKR manual (Arahan Teknik (Jalan) 5/85, 1985), the design
standard is classified into seven groups (R6, R5, R4, R3, R2 and R1) for rural areas and
seven groups (U6, U5, U4, U3, U2 and U1) for urban regions. These are arranged in
their descending order of hierarchy. Roads that provide long distance travel will require
a higher design speed whilst road which serves local traffic, where the issue of speed is
less significant can have a lower design speed. Also roads with heavier traffic will be