Modeling Risk-Related Knowledge in Tunneling Projects

• 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
• ISSN: 0272-4332; 1539-6924
• DOI: 10.1111/risa.12094

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.