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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

Same author

Modeling risk-related knowledge in tunneling projects.

Risk analysis : an official publication of the Society for Risk Analysis·2013

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Capturing and integrating knowledge for managing risks in tunnel works.

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

¹Department of Construction Management and Engineering, University of Twente, Enschede, The Netherlands. c.cardenas@utwente.nl.

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

|May 11, 2012

Summary

This summary is machine-generated.

This study introduces a method to capture and integrate construction risk knowledge using Bayesian Belief Networks (BBNs). It addresses information scarcity and expert bias to improve risk management in tunneling projects.

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Area of Science:

Construction Management
Risk Engineering
Decision Support Systems

Background:

Effective risk management in construction relies heavily on historical project data.
Scarcity of risk information is a significant challenge in construction projects.
Expert judgment is crucial but susceptible to biases during knowledge elicitation.

Purpose of the Study:

To propose and validate an approach for capturing and integrating risk-related knowledge in construction projects.
To utilize Bayesian Belief Networks (BBNs) to augment limited risk information with expert judgment.
To identify and mitigate biases in expert judgment elicitation for BBN construction.

Main Methods:

Development of a framework for knowledge capture and integration.
Elicitation of expert judgment from over 30 tunneling professionals in the Netherlands and Germany.
Construction and application of Bayesian Belief Networks (BBNs) to model top tunneling risks.
Analysis of judgment-based biases and implementation of mitigation strategies.

Main Results:

Six BBN models were developed for critical risks in tunnel works.
The approach successfully integrated expert knowledge to address information scarcity.
Bias mitigation strategies were identified and applied during expert judgment elicitation.
The method was illustrated using the specific case of face instability in soft soils with slurry shields.

Conclusions:

The proposed approach effectively captures and integrates risk knowledge for enhanced decision-making in construction.
Bayesian Belief Networks, augmented by expert judgment, provide a robust solution for managing risk information gaps.
Careful consideration of expert biases is essential for reliable BBN construction in risk management.
The validated models offer practical tools for managing specific tunneling risks.