Cost overrun risks are declared to be dynamic and interdependent. Ignoring the relationship between cost overrun risks during the risk assessment process is one of the primary reasons construction projects go over budget. Conversely, recent studies have failed to account for potential interrelationships between risk factors in their machine learning (ML) models. Additionally, the presented ML models are not interpretable. Thus, this study contributes to the entire ML process using a Bayesian network (BN) classifier model by considering the possible interactions between predictors, which are cost overrun risks, to predict cost overrun and assess cost overrun risks. Furthermore, this study compared the BN classifier model’s performance accuracy to that of the Naive Bayes (NB) and decision tree (DT) models to determine the effect of considering possible correlations between cost overrun risks on prediction accuracy. Moreover, the most critical risks and their relationships are identified by interpreting the learned BN model. The results indicated that the 18 BN models demonstrated an average prediction accuracy of 78.86%, significantly higher than the NB and DT. The present study identified the most significant risks as an increase in the cost of materials, lack of knowledge and experience among human resources, and inflation.

The problem of managing effective pedestrian evacuation during disruptions is a significant challenge because of the complexity and randomness of the passenger flow dynamics as well as the difficulty in predicting the responsive behaviour of the passengers. In this study, a hybrid agent-based and discrete-event simulation model of passenger flow is designed and implemented to assess the service level performance and the crowdedness ratio in a transfer metro station. This study investigates the statistical significance of the results obtained by the design alternatives and the safety analysis of a multiple-level transfer station through a real-world case study. The simulation model is designed to test various disruption scenarios, including train failure in the tunnel, as well as fire at the station gallery of a crowded metro station. The simulation result shows that the evacuation time can be decreased by 19.5%, compared with the basic design layout.