Developing a Scenario-Based Optimization Model for Planning Risks in Construction Projects by Integrating a Decision Support System with Bayesian Belief Network Analysis Approach: A Case Study in High-Rise Buildings

Abstract

With the increasing number of construction projects in modern times, there is a pressing need to effectively manage the three key aspects of a project: time, cost, and quality. Among these 3 aspects, time and cost aspects play a very important role in different stages of the project life cycle, because they can influence each other. Delays in the project can lead to increased costs, especially in countries suffering from inflation. Conversely, attempts to complete the project on time despite delays can result in a decrease in quality. Therefore, the aim of this research is to provide a risk assessment method based on Bayesian Belief network (BBN) to predict the delay and cost increase as well as the time and cost of the project and also to provide a decision support system (DSS) to determine the most suitable delay reduction strategy in terms of cost it was in construction project. The developed proposed framework was implemented in a 17-story tower and its results were compared with real data from the project. The findings show that the developed BBN has a high degree of accuracy in predicting project delays and cost overruns, and the proposed DDS can effectively determine the most suitable delay reduction strategy while keeping costs under control.