Related Experiment Videos
Using prior risk-related knowledge to support risk management decisions: lessons learnt from a tunneling project
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.
Summary
Probabilistic causal models for tunnel construction risks can be effectively applied to new projects, transferring valuable risk knowledge. These models identify critical risk factors, aiding risk management and remedial actions in underground construction.
Area of Science:
- Civil Engineering
- Risk Management
- Probabilistic Modeling
Background:
- Tunnel construction involves unique, context-dependent risks arising from ground conditions and project specifics.
- Existing risk data from past projects is often difficult to apply to new underground construction due to high variability.
- Probabilistic causal models offer a structured approach to represent and transfer complex risk-related knowledge.
Purpose of the Study:
- To investigate the application of pre-developed probabilistic causal models in a real tunnel construction case study.
- To assess the transferability and utility of project-specific risk knowledge using these models.
- To determine if these models can guide risk management and the selection of remedial measures.
Main Methods:
- Development of six probabilistic causal models for critical tunnel work risks (detailed in prior publications).
- Application and evaluation of these models within a specific tunnel construction case study.
- Identification and characterization of failure causes, conditions, interactions, and associated probabilistic information.
Main Results:
- Demonstrated that construction risk knowledge, formalized in probabilistic causal models, can be transferred between projects.
- The models successfully identified critical risk factors, providing valuable insights for the case study.
- The models support risk management decisions and guide the selection of appropriate remedial measures.
Conclusions:
- Probabilistic causal models are effective tools for transferring and utilizing risk-related knowledge in tunnel engineering.
- Despite inherent project and contextual challenges, these models enhance risk management in underground construction.
- The study discusses the limitations of the developed models for broader application.