A novel fuzzy dynamic Bayesian network for dynamic risk assessment and uncertainty propagation quantification in uncertainty environment. (September 2021)

• Record Type: Journal Article
• Authors: Guo, Xiaoxue; Ji, Jie; Khan, Faisal; Ding, Long; Tong, Qi
• Is Part Of: Safety science. Volume 141(2021)
• Publication Date: 2021-09
• Subjects: Fuzzy dynamic Bayesian network -- Data uncertainty -- Dynamic risk assessment -- Uncertainty propagation quantification; Industrial accidents -- Periodicals; Accident Prevention -- Periodicals; Safety -- Periodicals; Travail -- Accidents -- Périodiques; 363.11
• DOI: 10.1016/j.ssci.2021.105285
• Languages: English
• ISSNs: 0925-7535

Abstract

Highlights: A novel fuzzy dynamic Bayesian network (FDBN) model is proposed for dynamic risk assessment under uncertainty. The novel FDBN uses fuzzy numbers throughout the entire DBN modeling process. The novel FDBN enables to quantify uncertainty propagation over time. Quantitative probability ranges and risk ranges as well as the most likely values can be obtained. Results demonstrate credibility and reasonability of the novel FDBN for dynamic risk assessment. Abstract: Risk assessment (RA) plays a vital role in safety engineering. The conventional RA approaches have limited capabilities in handling time dependence and data uncertainty. Although dynamic Bayesian network (DBN) is robust in inference under uncertainty due to its flexible structure and capability of modeling the interdependencies of variables, it still has some defects in quantifying the uncertainty (probability range) propagation over time, and dealing with inaccurate or insufficient data (data uncertainty). This study is aimed to propose a novel fuzzy dynamic Bayesian network (FDBN) methodology to improve the ability of dynamic risk assessment (DRA) methods to quantify and propagate uncertainty arise from inaccurate or insufficient data. The methodology incorporates the fuzzy set theory (FST) with DBN to conduct DRA under data uncertainty while quantifying the uncertainty propagation over time. The proposed methodology represents the causality of variables in the time dimension and adopts expert elicitation and FST to determine the probability of causality. Triangular fuzzy numbers are used throughout the entire dynamic modeling process of DBN to completely retain the uncertainty information. A comparison between the proposed novel FDBN and crisp value based DBN verifies the credibility, rationality and robustness of the proposed methodology. A multi-variable risk assessment of the cotton warehouse is presented here to illustrate the potential of the proposed methodology in dealing with dynamic risk with uncertainty.