Bayesian Monte Carlo Simulation–Driven Approach for Construction Schedule Risk Inference

Abstract

As the construction of infrastructures becomes increasingly complex, it has been often challenged by construction delay with enormous losses. The delivery of complex infrastructures provides rich source of data for new opportunities to understand and address schedule issues. Based on these data, many efforts have been made to identify key construction schedule risks and predict the probability of risk occurrence. Bayesian network is one of the most useful tools for risk inference. However, there are still two obstacles preventing the Bayesian network from being adopted popularly in construction schedule risk management: (1) the development of directed acyclic graph (DAG) and associated conditional probability tables (CPTs); (2) the lack of observation data to trigger risk inference as evidence at the planning stage. This research aims to develop a novel Bayesian Monte Carlo simulation driven approach for construction schedule risk inference of infrastructures, where the Bayesian network model can be developed in a more convenient way and be used without observation data required. It firstly constructs the key risk network with key risks and links through network theory-based analysis. Then the DAG structure of Bayesian network is developed based on the topological structure of key risk network using deep-first search (DFS) and adapted maximum-weight spanning tree (A-MWST) algorithms. The CPTs are further developed using the leaky-MAX model. Finally, the Bayesian Monte Carlo simulation driven risk inference method is developed for predicting and quantifying the probability of construction schedule risk occurrence. A real infrastructure project is selected as case study to verify this developed approach. The results show that the developed approach is more appropriate to deal with risk inference of infrastructures considering its reliability, convenience, and flexibility. This research contributes a new way to construction schedule risk management and provides a novel approach for quantifying and predicting risk occurrence probability.

Analysis of the Schedule Risk of Prefabricated Buildings Based on ISM and Research of Transfer Path

“…The simulation results illustrate that the two-level principal-agent model can largely improve the completion probability and reduce the duration of an ITO project, thereby achieving the objective of effectively controlling the schedule risk ITO. Chen et al [10] develop a novel Bayesian Monte Carlo simulation-driven approach for construction schedule risk inference of infrastructures. In order to study the dynamics and uncertainty of risk, Xu et al [11] pioneers a combined SD and DES model for simulating the underlying schedule risks.…”

Managing Interdependence-Induced Systemic Risks in Infrastructure Projects

“…Despite their utility and effectiveness, current software packages are not designed to analyze and quantify contractors' interdependence. They are typically based on the principles of the critical path method for analyzing the interdependence among project activities (Wickwire and Smith 1974;Lagos and Alarcón 2021) as well as Monte Carlo analysis to perform statistical sampling of the risks affecting the project (Kwak and Ingall 2007;Chen et al 2021). Such tools consider the interdependence at the project activity level (e.g., using the Gantt chart) by connecting predecessor and successor activities as shown in Fig.…”

A Review on Key Performance Indicators for Measuring Real Estate Project Success

“…According to Gemünden et al [66], project success is measured by time, internal and external factors, budget, and quality. These factors, along with the Iron triangle are also very necessary to make a project successful [67]. However, according to the review of relevant literature, it has been noticed that many researchers have contended with the customs of plan success that emphasizes time, budget, and quality of the project, but many other factors hold a significant place in the project success [67,68].…”

“…These factors, along with the Iron triangle are also very necessary to make a project successful [67]. However, according to the review of relevant literature, it has been noticed that many researchers have contended with the customs of plan success that emphasizes time, budget, and quality of the project, but many other factors hold a significant place in the project success [67,68]. Moreover, five categories were identified by Ahadzie [62]: Internal and external environment related factors, customer satisfaction, quality, cost, and time-related factors.…”