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Due to the complex and dynamic atmosphere in the construction sector, different types of challenges are faced by project managers (PMs). These challenges potentially have negative impacts on the PM's managerial performance, which mostly leads to budget and schedule contingencies. In this vein, scrutinizing the main challenges in a construction project and identifying the cause-and-effect relationships among these challenges is a crucially important process. In the literature, a considerable number of papers have tried to determine construction PM challenges, mainly using statistical methods. These methods do not consider the cause-and-effect relationship among variables. To enhance the existing methods, this paper applies social network analysis (SNA) principles in order to rank a group of variables based on cause-and-effect relationships. To demonstrate the proposed idea, a data set is constructed that includes different types of challenges acquired from the literature comprehended with the forward-chaining approach. In total, 49 critical challenges were identified and subsequently categorized into 12 groups. Two questionnaires were designed to assist in ranking the challenges. 108 construction experts and 20 panelists participated in this study, and the acquired data were used to evaluate the proposed SNA-based method. By applying the proposed method to the obtained data, a complex weighted and directed network is constructed and examined by three metrics: weighted in-degree centrality, betweenness centrality, and closeness centrality. The results revealed that poor planning, contractors'/subcontractors' financial difficulties, and poor decision making are the main challenges that occur in the construction environment. Moreover, it was figured out that considering the cause-and-effect relationship among variables resulted in a highly different ranking of challenges, much closer to the real situation. This model could be used in quantitative-analytical research conducted in the construction project knowledge area in order to obtain more interpretable answers. Due to the complex and dynamic atmosphere in the construction sector, different types of challenges are faced by project managers (PMs). These challenges potentially have negative impacts on the PM's managerial performance, which mostly lead to time and cost overruns. In this vein, scrutinizing the main challenges in a construction project and identifying the cause-and-effect relationships among these challenges is a crucially important process. In this research, comprehensive scientific efforts were made in order to rank the main PMs' challenges in the construction sector, especially in developing countries. For this purpose, cause-and-effect relationships among variables were considered. By using different questionnaires as well as forming different focus groups and interviewing different experts, we found that the main top 10 challenges in the construction sector are: poor planning, contractors'/subcontractors' financial difficulties, poor decision making, time pressure, rework, stressful atmosphere, design alteration (even after execution), workforce turnover, fluctuation rate and economic instability, and inappropriate and unrealistic scheduling. [ABSTRACT FROM AUTHOR]

Despite its significance, the Indonesian construction industry has poor safety performance. Improving the safety climate has been seen as a way to improve safety in the industry. Research on safety climate in this context has identified a range of safety climate factors that require improvements. However, construction organizations face difficulties in implementing improvement recommendations due to resource constraints. In order to help construction organizations in their efforts to improve the safety climate, this research demonstrates the use of supervised classification approaches to identify specific safety climate factors that construction organizations should focus on. Data were collected from 311 construction practitioners in Indonesia using a 22-item safety climate survey. Supervised classification methods, comprising ensemble methods, Support Vector Machine, Naïve Bayes, and Nearest Neighbor, were used. The analysis identified 14 safety climate items that can represent the original dataset with high accuracy (93%). These 14 items can be considered crucial items that should be prioritized in the Indonesian construction industry. These items revealed that, due to the high power distance culture in Indonesia, top-down approaches, such as giving clear instructions, providing training, and reminding people often about safety, are effective for engaging employees to focus on and participate in safety. The findings also suggest that understanding cultural context is important to determine effective strategies to improve safety. This research has also demonstrated the potential application of supervised classification approaches to help decision makers improve safety by focusing on crucial factors within a context. [ABSTRACT FROM AUTHOR]

Finding a practical solution for the allocation of resources in ready-mixed concrete (RMC) is a challenging issue. In the literature, heuristic methods have been mostly used for solving the RMC problem. The introduced methods are intended to find a solution in one stage but the amount of infeasible allocations in their initial solutions is their main challenge, as these infeasible solutions need post-processing efforts. This paper introduces a sequential heuristic method that can solve RMC problems in two separate stages without any need for postprocessing. It was found that the depot-allocation problem is more complicated than truck allocation and the combination of these two subproblems threatens the efficiency of the solution. Another contribution of this paper is proposing a new formulation for minimizing the number of trucks. A genetic algorithm (GA) has been selected for implementing the proposed idea and for evaluating the large-scale data-set model. The data set covers an active RMC for a period of 1 month. The comprehensive tests show that sequential GA is more robust than traditional GA when it converges 10 times faster with achieved solution at 30% less cost. [ABSTRACT FROM AUTHOR]