Developing a Quantitative Modeling Framework for Risk Propagation Analysis: Application to Preconstruction Delays

Abstract

Research on preconstruction delays has been limited, mainly focusing on identifying delays and ranking risks using traditional methods. This study introduces a new quantitative framework to evaluate risks by modeling them as an interconnected network and conducting risk propagation analysis. The analytic hierarchy process was employed to quantify the interdependencies among 30 preconstruction delay risks based on survey inputs from 87 experts. Then, traditional risk quantification methods were extended by introducing two new metrics to account for the interdependencies between risks: re-evaluated likelihood and re-evaluated criticality. Subsequently, a topological analysis of the risk network was conducted to understand how risks propagate within the network. A risk reachability matrix was built to compute various node degree metrics, including in-degree, out-degree, the number of reachable nodes and possible sources, and betweenness centrality. Finally, clustering analysis and the TOPSIS method were applied to identify the most critical risks. Results showed that six risks were key contributors to preconstruction delays when factoring their interdependencies into the risk analysis, as follows: (1) insufficient commitment of project participants; (2) conflicts among project participants; (3) design changes, mistakes, errors, and omissions; (4) ineffective project planning and scheduling; (5) lack of enough funds/budget to finance project; and (6) ineffective team communication and slow information flow. This study adds to the body of knowledge by introducing a new framework for risk propagation analysis, enabling the analysis of preconstruction delay risks based on their interdependencies. The practical implications of this research equip practitioners and risk managers with a quantitative modeling framework to accurately identify and rank critical pre-construction delay risks based on their interconnectedness and causal relationships. This enables project stakeholders to allocate resources more efficiently and develop effective risk mitigation strategies.