A Hybrid Approach for Dynamic Simulation of Safety Risks in Mega Construction Projects. (23rd October 2020)

• Record Type: Journal Article
• Authors: Xu, Na; Liu, Qing; Ma, Ling; Deng, Yongliang; Chang, Hong; Ni, Guodong; Zhou, Zhe
• Other Names: Zhao Dong Academic Editor.
• Is Part Of: Advances in civil engineering. Volume 2020(2020)
• Publication Date: 2020-10-23
• Subjects: Civil engineering -- Periodicals; Civil engineering; Periodicals; 624
• DOI: 10.1155/2020/9603401
• Languages: English
• ISSNs: 1687-8086

Abstract

Abstract : Mega construction projects (MCPs) are inherently high-risk and complex. The challenge of safety management for mega construction projects is that safety risk factors constantly change and interact with each other in the long-term construction period. Few of the prior studies have enabled the prediction of the safety state in a dynamic and connected overview, which is a critical characteristic of safety risks in MCPs. Therefore, a hybrid approach for the dynamic simulation of risk factors is proposed. A three-stage procedure review of explicit documents, including accident investigation reports and construction standards, was carried out to identify safety risk factors and the causal relationships among them. Subsequently, the likelihood exposure and consequence (LEC) assessment method was applied to define the changes in risk factors over time. A system dynamics (SD) model was established to integrate the interacting risks and simulate the developing trend of the overall safety risk state. Moreover, a sensitivity analysis was provided to rank risk factors and simulate optimal risk mitigation strategies. Finally, the model was applied to the urban rail transit Line 9 project in China as a case study. The results indicated that the proposed hybrid approach performed satisfactorily under complex interrelated risk factors. Therefore, this study provides a practical framework to simulate and predict the safety state dynamically in a timely process for MCPs, either ahead of a project theoretically or during a project with real data.