J
Jingbing Zhong
Researcher at Huazhong University of Science and Technology
Publications - 5
Citations - 299
Jingbing Zhong is an academic researcher from Huazhong University of Science and Technology. The author has contributed to research in topics: Risk analysis & Bayesian network. The author has an hindex of 4, co-authored 5 publications receiving 232 citations.
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Journal ArticleDOI
Bayesian-network-based safety risk analysis in construction projects
Limao Zhang,Limao Zhang,Xianguo Wu,Miroslaw J. Skibniewski,Miroslaw J. Skibniewski,Jingbing Zhong,Yujie Lu +6 more
TL;DR: The proposed systemic decision support approach for safety risk analysis under uncertainty in tunnel construction can be used to provide guidelines for safety analysis and management in construction projects, and thus increase the likelihood of a successful project in a complex environment.
Journal ArticleDOI
Developing a cloud model based risk assessment methodology for tunnel-induced damage to existing pipelines
Limao Zhang,Limao Zhang,Xianguo Wu,Queqing Chen,Miroslaw J. Skibniewski,Miroslaw J. Skibniewski,Jingbing Zhong +6 more
TL;DR: This paper presents a cloud model (CM) based approach with step-by-step procedures for risk assessment of existing pipelines in tunneling environments (RAEPTE), where CM provides a basis for uncertainty transforming between qualitative concepts and their quantitative expressions.
Journal ArticleDOI
Dynamic risk analysis for adjacent buildings in tunneling environments: a Bayesian network based approach
Xianguo Wu,Zhou Jiang,Limao Zhang,Miroslaw J. Skibniewski,Miroslaw J. Skibniewski,Jingbing Zhong +5 more
TL;DR: A systemic Bayesian network (BN) based approach for dynamic risk analysis of adjacent buildings in tunneling environments, consisting of risk/hazard identification, BN learning and BN validation is presented.
Journal ArticleDOI
A Dynamic Decision Approach for Risk Analysis in Complex Projects
TL;DR: A systemic dynamic decision approach based on dynamic Bayesian networks (DBNs), aiming to provide guidelines for the dynamic safety analysis of the tunnel-induced road surface damage over time, demonstrates the feasibility of the proposed approach, as well as its application potential.
Proceedings ArticleDOI
Dynamic Risk Assessment in Construction Projects Using Bayesian Networks
TL;DR: In this article, a systemic Bayesian network (BN) based approach for dynamic risk assessment for adjacent buildings in tunnel construction is presented, which consists of four steps in detail, namely, hazard analysis, BN learning and BN-based risk analysis.