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Using Prior Risk‐Related Knowledge to Support Risk Management Decisions: Lessons Learnt from a Tunneling Project

Author

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  • Ibsen Chivatá Cárdenas
  • Saad S. H. Al‐Jibouri
  • Johannes I. M. Halman
  • Wim van de Linde
  • Frank Kaalberg

Abstract

The authors of this article have developed six probabilistic causal models for critical risks in tunnel works. The details of the models' development and evaluation were reported in two earlier publications of this journal. Accordingly, as a remaining step, this article is focused on the investigation into the use of these models in a real case study project. The use of the models is challenging given the need to provide information on risks that usually are both project and context dependent. The latter is of particular concern in underground construction projects. Tunnel risks are the consequences of interactions between site‐ and project‐ specific factors. Large variations and uncertainties in ground conditions as well as project singularities give rise to particular risk factors with very specific impacts. These circumstances mean that existing risk information, gathered from previous projects, is extremely difficult to use in other projects. This article considers these issues and addresses the extent to which prior risk‐related knowledge, in the form of causal models, as the models developed for the investigation, can be used to provide useful risk information for the case study project. The identification and characterization of the causes and conditions that lead to failures and their interactions as well as their associated probabilistic information is assumed to be risk‐related knowledge in this article. It is shown that, irrespective of existing constraints on using information and knowledge from past experiences, construction risk‐related knowledge can be transferred and used from project to project in the form of comprehensive models based on probabilistic‐causal relationships. The article also shows that the developed models provide guidance as to the use of specific remedial measures by means of the identification of critical risk factors, and therefore they support risk management decisions. Similarly, a number of limitations of the models are discussed.

Suggested Citation

  • Ibsen Chivatá Cárdenas & Saad S. H. Al‐Jibouri & Johannes I. M. Halman & Wim van de Linde & Frank Kaalberg, 2014. "Using Prior Risk‐Related Knowledge to Support Risk Management Decisions: Lessons Learnt from a Tunneling Project," Risk Analysis, John Wiley & Sons, vol. 34(10), pages 1923-1943, October.
    • Handle: RePEc:wly:riskan:v:34:y:2014:i:10:p:1923-1943
    DOI: 10.1111/risa.12213
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    References listed on IDEAS

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    1. Andersen, L.B. & Häger, D. & Maberg, S. & Næss, M.B. & Tungland, M., 2012. "The financial crisis in an operational risk management context—A review of causes and influencing factors," Reliability Engineering and System Safety, Elsevier, vol. 105(C), pages 3-12.
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    5. Francis Adams, 2006. "Expert elicitation and Bayesian analysis of construction contract risks: an investigation," Construction Management and Economics, Taylor & Francis Journals, vol. 24(1), pages 81-96.
    6. Ibsen Chivatá Cárdenas & Saad S.H. Al‐jibouri & Johannes I.M. Halman & Frits A. van Tol, 2013. "Capturing and Integrating Knowledge for Managing Risks in Tunnel Works," Risk Analysis, John Wiley & Sons, vol. 33(1), pages 92-108, January.
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    Cited by:

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    2. Wang, Fan & Li, Heng & Dong, Chao & Ding, Lieyun, 2019. "Knowledge representation using non-parametric Bayesian networks for tunneling risk analysis," Reliability Engineering and System Safety, Elsevier, vol. 191(C).

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