Abstract. Shortage and delay in materials supply is argued to be one of the most important factors that lead to delay in construction project delivery globally. However, the relevant underlying reasons vary from country to country. As such, this paper summarises the outcomes of a study that targeted identifying causes of shortage and delay in materials supply in Brunei Darussalam. The study was conducted through fifteen semi-structured interviews of contractors and materials suppliers in Brunei. The study identified six causes of shortageof materials and nine causes of delay in materials supply in Brunei.The most importantcausefor shortage of materials relates to the origin or availability of construction materials. On the other hand, the most influential cause of delay in material supply was found to be poor materials procurement and inventory management system, which has other underlying reasons such as late identification of the type of materials needed. The observations are expected to help in formulating or reviewing relevant policies, in order to ensure on-time project delivery. IntroductionDelays are common in construction projects. For example, Morris and Hough [1] examined more than four thousand construction projects from UK and Europe and observed that projects were rarely completed on schedule time. Similar outcomes were observed in many other countries as well[2-6]. There are many causes for such project delays, of which shortage and delay in materials supply are among the most notable in many studies. For example, Assaf et al. [4] studied the causes of delay in large building projects in Saudi Arabia and identified a group of factors relating to materials, which included causes related to shortages and delivery of materials. Abd Majid and McCaffer [7] observed that late delivery and slow mobilization of materials ranked 1 among 25 factors contributing to causes of non-excusable construction delays in United Kingdom. Koushki and Kartam[8] studied 450 small, medium and large private residential projects in Kuwait and found that nearly one-fourth of the total project delays were due to the late delivery of materials. Similarly, studies in Nigeria, Egypt, observed that delay in supply, and/or shortage of, materials caused the project delay. It was also observed in Brunei that shortage and delay of construction materials were the leading causes of project delay [13].Whilestudies in many other countries attempted to identify the causes of delay or shortage of materials, no such study was previously conducted in Brunei, at least to the knowledge of the authors. As such, the present study was undertaken to identify the causes of delays insupplyandshortageofmaterialsinBruneiconstruction.
Abstract. Road T raffic Accidents (RT A) are known to be one of the main causes of fatalities worldwide. One usef ul approach to improve road safety is through the identification of RT A hotspots along a road, so they can be prioritised and treated. T his paper introduces an approach based on Geographical Information System (GI S) to identify and prioritise RT A hotspots along a road network using historical RT A data. One particular urban road in Brunei with a historically high rate of RT As, Jalan Gadong, was selected as a case study. Five years of historical RT A data were acquired from the relevant authorities and input into a GIS database. GI S analysis was then used to identify the spatial extension of the RT A hotspots. The RT A hotspots were ranked according to three different schemes: frequency, severity and socio-economic impact of RT As. A composite ranking scheme was also developed to combine these schemes; this enabled the prioritisation and development of intervention and maintenance programmes of the identified RT A hotspots. A visualisation method of the RT A spatial distribution within each identified RT A hotspot was also developed to determine the most risky road stretches within each hotspot, which is important for treatment prioritisation when limited resources are available.
1Accurate roundabout capacity models are essential for optimal roundabout 2 designs, but there exists significant differences in the predicted capacities of various 3 state-of-the-art models and in their included explanatory variables. An empirical 4 study into roundabout lane entry capacity was thus performed in the U.K. using data 5 from 35 roundabout entry lanes, where various model forms and explanatory 6 variable sets were tested. Two regression models and an artificial neural network 7 were developed. A negative exponential relationship with circulating flow predicted 8 lane capacity better at high and low circulating flows, and better reflected the overall 9 trends in the aggregated capacity data compared to a linear model. The regression 10 models performed relatively well, and provided better information on the impacts of 11 the variables than the neural network. The models consistently suggest that entry-12 exit separation and flows exiting on the same arm have stronger significant effects 13
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