چكيده به لاتين
Delay in mass housing projects is one of the important challenges in this field. This delay can lead to increased costs, decreased customer satisfaction and financial losses. Therefore, it is essential to assess the risks of delay in these projects. On the other hand, among the existing risk assessment methods (Artificial Neural Network (ANN), Monte Carlo simulation, Analytical Network Process (ANP), Structural Equation Modeling (SEM), System Dynamics (SD) and Bayesian Belief Network (BBN) and...), system dynamics method (SD) and Bayesian Belief Network (BBN), in addition to providing the possibility of analyzing the interaction between risk factors, have also been used to quantitatively evaluate the effects of risks. Although the Bayesian Belief Network (BBN) has better accuracy in quantitative evaluation than the System Dynamics (SD) method, it still has limitations such as the lack of systematic structure and the large amount of data required when filling conditional probability tables (CPT) in studies. In this research, the Bayesian Belief Network (BBN) method was used and by using the combination of interpretive structural modeling and testing and evaluation of decision-making (Ism-Dematel), the limitations of previous studies in the evaluation of delay risks were resolved and for the study of the project mass housing was used. Then, the ranked node method (RNM) was used to complete the parametric part of the Bayesian Belief Network (BBN) using the same data obtained from experts for the analysis of experiments and decision evaluation (Dematel). AGENA RISK software was also used to run the model. The objectives of this study are: (1) to reduce the number of questions and the time and effort required to complete the Bayesian Belief Network (BBN) parameters, (2) to provide a simple and understandable method to construct the Bayesian Belief Network (BBN) structure based on knowledge specialized and (3) review and assess the risks of delay in mass housing projects. The findings showed that compared to the traditional Bayesian Belief Network (BBN), the proposed method significantly reduced the time and effort needed to extract network parameters and made it easy to create a Bayesian Belief Network (BBN) structure. The results obtained from the implementation of the model on a mass housing project state that taking into account the identified risk factors, delay risk factors related to the contractor, employer payments, inflation, price fluctuations, and insufficient experience of the consultant can have an important effect on increasing the project completion time. This research can help the managers of mass housing projects to identify the factors affecting the delay, the relationship between them and the importance of each one, to take the necessary measures to reduce these risks and pay special attention to the risk factors.
