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Using prior risk-related knowledge to support risk management decisions: lessons learnt from a tunneling project.

Risk analysis : an official publication of the Society for Risk Analysis·2014

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Related Experiment Video

Updated: May 9, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Modeling risk-related knowledge in tunneling projects.

Ibsen Chivatá Cárdenas¹, Saad S H Al-Jibouri, Johannes I M Halman

¹Department of Construction Management and Engineering, University of Twente, P.O. Box 217, 7500 AE, Enschede, The Netherlands.

Risk Analysis : an Official Publication of the Society for Risk Analysis

|July 20, 2013

Summary

This summary is machine-generated.

This study integrates expert knowledge on tunnel construction failure events using Bayesian belief networks. The developed models provide guidance for risk management and selecting remedial measures.

Keywords:

Bayesian belief networks epistemic uncertainty relevant information reliability modeling risk modeling risk-related knowledge modeling

Related Experiment Videos

Last Updated: May 9, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Area of Science:

Civil Engineering
Risk Management
Decision Science

Background:

Construction project failure data is valuable for risk management but often scarce, undocumented, or unavailable.
Existing methods for integrating and analyzing this knowledge are lacking or not cost-effective.

Purpose of the Study:

To demonstrate methods for integrating knowledge on critical potential failure events in tunnel works.
To overcome challenges of unavailable or incomplete information in risk assessment.

Main Methods:

Eliciting expert judgments on failure scenarios and probabilistic information.
Utilizing customized Bayesian belief-network-based models to integrate expert knowledge.
Addressing epistemic uncertainty in risk assessment through model customization.

Main Results:

Successfully integrated expert knowledge on potential failure events in tunnel construction.
Developed models capable of handling divergent expert judgments and epistemic uncertainty.
Demonstrated the utility of integrated risk knowledge in guiding remedial measure selection.

Conclusions:

Expert knowledge integration using Bayesian belief networks is feasible for tunnel construction risk management.
Customized models effectively address uncertainty and expert judgment divergence.
The approach provides practical guidance for implementing specific remedial measures to mitigate risks.