ميثم عشقي دشمن كردهي

Due to the ever-increasing demand for housing, the initial solution adopted by governments in response to this growing need is mass construction. Efforts to achieve cost-effective production as the main objective of such projects may often be overshadowed by persistent cost overruns. One critical criterion for assessing the success of an industrial project is the completion and delivery of the project in accordance with the agreed-upon terms. However, construction industry projects frequently encounter rising costs. Previous research highlights two main issues: 1) the mutual relationships between various factors have been largely overlooked, and 2) there has been a lack of effective measurement of risk response strategies. In this research, leveraging Bayesian networks allows us to address these limitations by examining a project characterized by excessive costs and making informed decisions regarding modeling strategies. The proposed model is specifically applied to a mass housing construction project. This study aims to identify the critical features of the Fuzzy eva‎luation Score (FRS) method, derive the risk causal network, and develop the conditional probability tables (CPTs) utilizing an integrated Fuzzy DEMATEL-ISM approach alongside the Ranked Node Method (RNM) for prioritizing expenditures. Furthermore, the Fuzzy Best-Worst Method (FBWM) was employed to ascertain requirements through structured questionnaires and standardized methods applied among seven experts. The findings of this research revealed that fluctuations in the prices of materials and equipment, delays in their delivery, economic instability, inadequacies in tender processes, and deficiencies in planning and timing were the most significant risk factors in the studied project, with respective risk scores of 0.872, 0.854, 0.806, 0.790, and 0.771. Notably, Fuzzy DEMATEL emphasized that weaknesses in planning and timing contribute most substantially to cost overruns. Additionally, the results indicated that the most effective strategy for minimizing the likelihood of cost overruns—considered optimal at a condition level of 100%—is to prioritize investments in strategies aimed at enhancing planning and scheduling, achieving a high effectiveness condition of 75%.

ارزيابي ريسك , مديريت ريسك , استراتژي هاي پاسخ به ريسك , شبكه هاي بيزين فازي , ريسك هاي مؤثر بر هزينه

risk assessment , risk management , Risk Response , Fuzzy Bayesian Belief Networks (FBBNs) , Cost Overrun risks

Meysam Eshghi Doshman Kordehi

Ali Akbar Shirzadi Javid, PhD