Modeling Risk-Related Knowledge in Tunneling Projects

Knowledge on failure events and their associated factors, gained from past construction projects, is regarded as potentially extremely useful in risk management. However, a number of circumstances are constraining its wider use. Such knowledge is usually scarce, seldom documented, and even unavailab...

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Bibliographic Details
Published in:Risk analysis 2014-02, Vol.34 (2), p.323-339
Main Authors: Cárdenas, Ibsen Chivatá, Al-Jibouri, Saad S.H., Halman, Johannes I.M., van Tol, Frits A.
Format: Article
Language:English
Subjects:
Applied sciences
Bayesian belief networks
Bayesian method
Biological and medical sciences
Buildings. Public works
Constraining
Construction industry
Cost analysis
epistemic uncertainty
Exact sciences and technology
Failure
Judgement
Judgments
Knowledge
Mathematical models
Medical sciences
Modelling
Public health. Hygiene-occupational medicine
relevant information
reliability modeling
Risk
Risk analysis
Risk assessment
Risk management
risk modeling
risk-related knowledge modeling
Studies
Tunnels
Tunnels, galleries
Uncertainty
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Summary:Knowledge on failure events and their associated factors, gained from past construction projects, is regarded as potentially extremely useful in risk management. However, a number of circumstances are constraining its wider use. Such knowledge is usually scarce, seldom documented, and even unavailable when it is required. Further, there exists a lack of proven methods to integrate and analyze it in a cost‐effective way. This article addresses possible options to overcome these difficulties. Focusing on limited but critical potential failure events, the article demonstrates how knowledge on a number of important potential failure events in tunnel works can be integrated. The problem of unavailable or incomplete information was addressed by gathering judgments from a group of experts. The elicited expert knowledge consisted of failure scenarios and associated probabilistic information. This information was integrated using Bayesian belief‐networks‐based models that were first customized in order to deal with the expected divergence in judgments caused by epistemic uncertainty of risks. The work described in the article shows that the developed models that integrate risk‐related knowledge provide guidance as to the use of specific remedial measures.
ISSN:0272-4332
1539-6924
DOI:10.1111/risa.12094