A risk assessment method of deep excavation based on Bayesian analysis and expert elicitation

Ning Wang (), Cheng-shun Xu (), Xiu-li Du () and Ming-ju Zhang ()
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Ning Wang: Beijing University of Technology
Cheng-shun Xu: Beijing University of Technology
Xiu-li Du: Beijing University of Technology
Ming-ju Zhang: Beijing University of Technology

International Journal of System Assurance Engineering and Management, 2018, vol. 9, issue 2, No 11, 452-466

Abstract: Abstract In project management, it is an important approach that the deep excavation safety risk (DESR) is synthetically assessed based on experiential information provided by experts. This information is usually obtained from multiple sources and is incomplete. Therefore, the problem of synthesizing multiple sources and uncertainty information is of great significance for the assessment and control of DESR. This paper presents a risk assessment and decision approach for deep excavation construction based on Bayesian analysis. First, the expressions of a likelihood function used to describe and synthesize uncertain information obtained from expert panel is constructed, and a discretization interval division method of occurrence probability and consequences of an unwanted event is proposed based on the current risk code in China. Then, the discrete model of synthesizing information of expert estimates is constructed, and a comparative analysis of expert-elicited results and formula deduction are utilized to evaluate the advantages of this model under some hypothetical scenarios. On that basis, the index system of DESR is established and the weight of each index is calculated by the method of analytic hierarchy process. The risk matrix, deviation degree and risk acceptance criteria are developed to determine the whole risk level. Finally, the proposed method is verified by analyzing a typical deep excavation of Beijing Metro. The results demonstrate the feasibility of this method and its application potential.

Keywords: Evaluation of construction safety risk; Deep excavation; Bayesian analysis; Expert elicitation (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s13198-017-0689-2

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