LIST OF EQUATIONS
4. Chapter 4 Methodology
5.3. Study Background
A primary concern in this study is to achieve a reliable data set to develop reliable results. A summary of essential roads under the jurisdiction of the Ministry of Public Works in the UAE is highlighted in Appendix 1. An example is shown in the table below.
A summary of the length of roads used in the study is also given in Appendix 1. The roads to be investigated are selected based on the data availability. Table 5-1 shows an example list.
Table 5-1: Example of the roads selected based on the data availability by Valor (2013)
N Code Road Direction Length [m]
1 E.11 E-11. Ittihad road RAK 47,560
2 E.11 E-11. Ittihad road SHA 47,650
3 E18.1 E-18. Manama- RAK Airport Rak Airport 41,640
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4 E18.1 E-18. Manama- RAK Airport Manama 41,550
5 E18.2 E-18. RAK Airport-Sha'am Sha'am 53,360
According to the design manual followed by the UAE Ministry of Public Works, in terms of Data Assets Management, data are collected and organised by considering that all the nominated roads have a starting point and a final point. Some roads were divided into three sub-roads based on direction. For example, the E99 highway has been divided into three segments, E-99.1 (from Khor Fakkan to Dibba), E-99.2 (from Fujairah to Khor Fakkan) and E-99.3 (from Fujairah to Oman Border).
To achieve a better data analysis for the road classification, it was decided to split the roads into two groups for data collection, named forward direction roads and backward direction roads, as per the tables in Appendix 1 (Valor 2013). An example is shown in Tables 5-2 and 5-3 for illustration purpose. Classification of the backward and forward roads follows the UAE Ministry of Public Works. Therefore, the two sets of data are organised accordingly.
Table 5-2: Example of roads classified as forward direction group based on Valor (2013) N Road
Code
Road Direction Length [m] Data Collection Approach
2 E.11 Ittihad road SHA 47,650 Forward
4 E18.1 Manama- RAK Airport Manama 41,550 Forward
6 E18.2 RAK Airport-Sha'am Rak Airport 53,480 Forward
Table 5-3: Example of roads classified as backward direction group based on Valor (2013) N Road
Code
Road Direction Length [m] Data collection Approach
1 E.11 Ittihad road RAK 47,560 Backward
3 E18.1 Manama- RAK Airport Rak Airport 41,640 Backward
5 E18.2 RAK Airport-Sha'am Sha'am 53,360 Backward
7 E311 Sheik Mohammed Bin Zayed SHA 70,950 Backward
In order to build an effective and rigorous pavement deterioration model, there is a need for appropriate input data. Such data include information about inventory, for example, location and structural type of individual components. In general, Sun,
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Bocchini and Davison (2018) stated that data may be gathered from the field, or generated from simulations, experiments and expert surveys. However, having access to such data is difficult as some companies and highway agencies refuse to share their inventory data. Sun, Bocchini and Davison (2018) highlighted that national security and competitive advantage are the main reasons for not sharing these data. They also added that general scarcity of data leads to limited data validation and consequently difficulties in ensuring the accuracy of a deterioration model.
Every highway agency has a different approach to data collection and storing.
According to the asset condition manual followed by the UAE Ministry of Public Works, in terms of Data Assets Management, the approach for data collection was based on lane numbering, as shown in Figure 5-4. The odd numbers were assigned to the lanes defined in the carriageway as moving from the starting point to the ending point of the road (forward direction), for example, the fast lane named as number 5 and the slow lane designated as number 1. On the other hand, the pair numbers were defined in the carriageway coming from the road ending point to the starting point (backward direction). The first fast lane was numbered 6, and the most right-sided lane (slow lane) was numbered 2 (Valor 2013). According to UAE traffic law, heavy vehicles are only allowed to travel in a slow lane. TRL Road Note 31 (TRL 1993) states that frequent heavy axle loads and slow-moving heavy traffic are the leading causes for road sections to undergo surface rutting. For this research, only data occurring in slow lanes (number 1 and number 2), which are the lanes with an excessive proportion of heavy goods vehicles, are considered in this research. These sections are prone to fast deterioration activities. Therefore, the model was built on the data generated from these slow lanes. Anyala (2011) also focused on the road used by heavy vehicles is his study. A similar approach is followed in this research.
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Figure 5-4: Data collection methodology for the selected roads (Valor 2013)
A complete inventory of all roads was obtained from the Ministry of Public Works and Al Ain City Municipality. In each section, data were collected for every 10 m for both directions with the focus on the two slow lanes. For instance, data for the forward direction included slow lane number 1 and the same approach was used for the backward direction with slow lane number 2.
In his study, Mubaraki (2010) used a sample unit of pavement distress such as cracking and rutting for the selected main roads of every 100-metre length. Anyala (2011) also applied the same as he used averaged rutting depth data over 100 m road sections on the road carriageway. It is worth pointing out that both studies used infrastructures that are very old. The infrastructure used in this study is still in very good condition. The author carried out a similar approach to analyse the received data.
The average sample for the 100-metre section was taken into the analysis to define the sample value for International Roughness Index (IRI), cracking, rutting and Pavement Condition Index (PCI). The number of readings for each road was substantial. The total length of the network was 1,060,090 linear metres (1060 km) (see the Appendix 1 for road classification). Data analysis demonstrated that there is no variation in IRI, rutting and cracking at 100-metre interval sampling. This could be due to the fact that the network is new, as stated above. Several sampling experimental tests were conducted. The author was able to estimate the value of IRI, rutting and cracking based on average reading of 5000 metres (more details are provided in Chapter 6 section 6.3).
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