LIST OF EQUATIONS
4. Chapter 4 Methodology
5.13. Voids in Mix (VIM)
The author has also followed the equation proposed by Anyala (2011). The reason behind this is that there are no recorded data that represent the voids in mix (VIM) concerning pavement age. Moreover, a similar coefficient will be used in this research, which is B1= -0.07 and B2= 1.39. The determination of VIM is shown in the table in Appendix 1. Samples of the results are given in tables 5-35 and 5-36.
Table 5-35: Example of development of voids in mix (VIM) based on data collection backward N Road
Code
Road constructio n date
Major rehabilitati
on
road age
b1 b2 VIM%
1 E.11 Ittihad road 2006 no record 11 -0.07 1.39 1.22
3 E18.1 Manama- RAK
Airport
2006 no record 11 -0.07 1.39 1.22
N Road Code
Road
constructio n date Major rehabilitati on road age a1 a2 softening point
1 E.11 Ittihad road 2006 N/A 11 2.5 70.5 76.5
3 E18.1 Manama- RAK Airport 2006 N/A 11 2.5 70.5 76.5
5 E18.2 RAK Airport-Sha'am 2005 N/A 12 2.5 70.5 76.8
160
5 E18.2 RAK Airport-
Sha'am
2005 no record 12 -0.07 1.39 1.22
7 E311 Sheik Mohammed
Bin Zayed
2011 no record 6 -0.07 1.39 1.26
Table 5-36: Example of development of voids in mix (VIM) based on data collection forward N Road
Code
Road construction date
Major rehabilitation
road age
b1 b2 VIM
2 E.11 Ittihad road 2006 no record 11 -0.07 1.39 1.22
4 E18.1 Manama- RAK
Airport
2006 no record 11 -0.07 1.39 1.22
6 E18.2 RAK Airport- Sha'am
2005 no record 12 -0.07 1.39 1.22
5.14. Pavement Failure Risk 5.14.1. Risk Rating Matrix
A risk matrix is applied to plot the pavement deterioration risk. The pavement risk factor was recorded and measured based on the likelihood and impact rating of each risk event. Mapping risks in a matrix for all the elements related to pavement failure (pavement deterioration) is discussed in Chapter 2. Generally, a questionnaire is a set of questions used to obtain the specific information and data required for fulfilment of a study’s research objective (Parasuraman, Zeithaml and Berry 1991).
The survey comprises different phases such as designing the questionnaire, distributing the design and, finally, collecting the completed survey forms for the desired research investigations. It is a fast process compared to other research methods. In this study, the researcher did introduce a probability rating as shown in Figure 5-14. The researcher designed a questionnaire survey based on the probability rating scaling the risk contribution from 0.1 to 0.72. It was proposed that the questionnaire survey would be completed by experts in the area of pavement engineering such as pavement material engineers, highway maintenance contractors, highway maintenance consultants, clients, asset managers, etc. The nominated experts
161
were asked to complete the questions to achieve a constant scale of magnitude to rate and quantify the expected risk effect for each risk event.
Figure 5-14: Risk matrix with risk effect scale numbering developed by the author
The primary objective of the questionnaire survey is to measure the expected risk effect of pavement failure due to climate change. The results quantify variable risk relationships. The plan for the questionnaire survey is provided in Appendix 2.
Approximately 30 questionnaires were received from experts in the area of pavement engineering such as pavement material engineers, highway maintenance contractors, highway maintenance consultants, clients and asset managers. Such an approach is to reduce the potential bias arising from an individual judgement. The results of the questionnaires are shown in Appendix 2. The list of experts who participated in the survey is presented in Figure 5-15, while their years of experience are provided in Figure 5-16.
5.14.2. Respondents’ General Information
Even though the target sample was 40 participants, requests to participate in the survey were made to 50 participants working in the asphalt field sector of the construction industry in the UAE. However, only 30 returned valid questionnaires with all sections fully responded to. Figures 5-15 and 5-16 summarise the participants’
information.
Probability
0.05 0.09 0.18 0.36 0.72
( ) ( ) ( ) ( ) ( )
0.04 0.07 0.14 0.28 0.56
( ) ( ) ( ) ( ) ( )
0.03 0.05 0.1 0.2 0.4
( ) ( ) ( ) ( ) ( )
0.02 0.03 0.06 0.12 0.24
( ) ( ) ( ) ( ) ( )
0.01 0.01 0.02 0.04 0.08
( ) ( ) ( ) ( ) ( ) 0.05/ very
low 0.10/ low 0.2/low 0.04/High 0.80/Very High 0.1
Threats
0.9
0.7
0.5
0.3
162
Figure 5-15 Number of experts in the area of pavement engineering that participated in the questionnaire survey
Figure 5-16: Experience of experts in the area of pavement engineering that participated in the questionnaire survey
5.14.3. Descriptive Statistics for Pavement Failure Risk
The primary objective of descriptive statistics is to handle the process of data organising, summarising and presenting to achieve a very convenient and informative set of data (Keller 2009). Descriptive statistics is the best technique to describe the key structures of a collection of data in quantitative terms. In terms of data analysis, Mubaraki (2010) stated that estimation of a parameter for the distribution, to characterise the spread or variability, is an essential task in exploratory data analysis.
For example, the mean is the most accepted measure of the central tendency of a distribution of results. Other parameters use the median, which defines the midpoint of distribution, or standard deviation, which is also the most accepted measure of the
0 1 2 3 4 5 6 7 8 9 10
Material Engineer Project Manager supplier Asset Engineer Material Technician
Project Engineer Asset Manager Resident Engineer Maintenance Engineer
Number of experts in the area of pavement engineering that participated in the questionnaire survey
0 5 10 15 20
More 12 years ( 8-12 )years
(6-8) years (2- 5)years (0- 1)years
participants
Experience of experts in the area of pavement engineering that participated in the questionnaire survey
163
variability of a distribution. Further characterisation of the data including skewness and kurtosis can also be introduced to define the lack of symmetry and whether the data are peaked or flat with respect to a normal distribution respectively.
The table (5-37) below explains the descriptive statistical analysis for significant risk associated with pavement failure. Such a study was carried out with the aid of the SPSS software program. Data preparation and cleaning such as removing any invalid data or outliers was carried out to obtain the most solid sample.
The descriptive statistical analysis in this section includes measurement aspects of non-linear and linear regression analysis. The primary objective is to investigate the most reliable coefficient based on the available data gathered from the questionnaire on pavement failure risk. Further descriptive statistical analysis is presented to examine every single risk and related sub-risk. The Major risks associated with pavement failure due to climate change impacts are highlited in Table 5-38. More details are provided in Chapter 9 section 9.4.
Table 5-37: Descriptive statistics for major risk associated with pavement failure due to climate change impacts
N Minimu m
Maxi mum
Sum Mean Std.
Deviation
Varian ce
Skewness Kurtosis
Y1 30 0.01 0.36 4.86 0.162 0.09845 0.01 0.426 0.42 -0.734 0.833
Y2 30 0.02 0.72 8.35 0.278 0.2452 0.06 0.528 0.42 -1.158 0.833
Y4 30 0.03 0.56 3.97 0.132 0.11782 0.014 1.948 0.42 4.734 0.833
Y3 30 0.01 0.4 4.49 0.149 0.10074 0.01 1.07 0.42 0.597 0.833
Y5 30 0.02 0.56 5.09 0.169 0.12181 0.015 1.44 0.42 2.68 0.833
Y6 30 0.01 0.28 3.7 0.123 0.08759 0.008 0.44 0.42 -1.176 0.833
Y7 30 0 1 5 0.17 0.127 0.016 1.286 0.42 1.878 0.833
Y8 30 0.01 0.4 4.4 0.146 0.10104 0.01 0.661 0.42 -0.35 0.833
Y9 30 0.01 0.56 7.37 0.245 0.16021 0.026 0.068 0.42 -0.814 0.833
Y1 0
30 0.01 0.72 6.11 0.2037 0.19468 0.038 1.44 0.42 1.709 0.833
Y1 1
30 0.03 0.8 8.86 0.2953 0.25833 0.067 0.79 0.42 -0.994 0.833
Y1 2
30 0.01 0.4 2.32 0.0773 0.08741 0.008 2.274 0.42 6.089 0.833
Y1 3
30 0.01 0.72 7.21 0.2403 0.21386 0.046 0.969 0.42 -0.188 0.833
164
Table 5-38: Major risks associated with pavement failure due to climate change impacts
Y1 R1.Traffic
Y2 R2.Climate Change
Y3 R3.Pavement Composition
Y4 R4.Pavement Strength
Y5 R5.Pavement Ageing
Y6 R6.Subgrade Soil
Y7 R7.Drainage
Y8 R8.Maintenance
Y9 R9.Construction Quality
Y10 Rutting
Y11 Cracking
Y12 S7.Vehicle speed
Y13 S19.Pavement Thickness