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Capturing and Integrating Knowledge for Managing Risks in Tunnel Works

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  • Ibsen Chivatá Cárdenas
  • Saad S.H. Al‐jibouri
  • Johannes I.M. Halman
  • Frits A. van Tol

Abstract

Risk‐related knowledge gained from past construction projects is regarded as potentially extremely useful in risk management. This article describes a proposed approach to capture and integrate risk‐related knowledge to support decision making in construction projects. To ameliorate the problem related to the scarcity of risks information often encountered in construction projects, Bayesian Belief Networks are used and expert judgment is elicited to augment available information. Particularly, the article provides an overview of judgment‐based biases that can appear in the elicitation of judgments for constructing Bayesian Networks and the provisos that can be made in this respect to minimize these types of bias. The proposed approach is successfully applied to develop six models for top risks in tunnel works. More than 30 tunneling experts in the Netherlands and Germany were involved in the investigation to provide information on identifying relevant scenarios than can lead to failure events associated with tunneling risks. The article has provided an illustration of the applicability of the developed approach for the case of “face instability in soft soils using slurry shields.”

Suggested Citation

  • 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.
    • Handle: RePEc:wly:riskan:v:33:y:2013:i:1:p:92-108
    DOI: 10.1111/j.1539-6924.2012.01829.x
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    Cited by:

    1. Crispim, José & Fernandes, Jorge & Rego, Nazaré, 2020. "Customized risk assessment in military shipbuilding," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    2. 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.
    3. Brouwer, Sander R. & Al-Jibouri, Saad H.S. & Cárdenas, Ibsen Chivatá & Halman, Johannes I.M., 2018. "Towards analysing risks to public safety from wind turbines," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 77-87.
    4. Ibsen Chivatá Cárdenas & Saad S.H. Al‐Jibouri & Johannes I.M. Halman & Frits A. van Tol, 2014. "Modeling Risk‐Related Knowledge in Tunneling Projects," Risk Analysis, John Wiley & Sons, vol. 34(2), pages 323-339, February.
    5. Limao Zhang & Xianguo Wu & Yawei Qin & Miroslaw J. Skibniewski & Wenli Liu, 2016. "Towards a Fuzzy Bayesian Network Based Approach for Safety Risk Analysis of Tunnel‐Induced Pipeline Damage," Risk Analysis, John Wiley & Sons, vol. 36(2), pages 278-301, February.
    6. 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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