Bayesian network based decision support for predicting and mitigating delay risk in TBM tunnel projects

Date

2021-09-01

Author

Koseoglu Balta, G.C.
Dikmen Toker, İrem
Birgönül, Mustafa Talat

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Tunnel projects involve high levels of uncertainty stemming from the vagueness of geological conditions and the complexity of the mechanized tunnel boring process. Delay risk assessment is carried out by project managers to identify critical risk factors leading to time and cost overruns and formulate strategies to meet the project targets under different scenarios. In this study, a Bayesian Belief Network (BBN) based risk assessment method was developed for Tunnel Boring Machine (TBM) tunnel projects to predict delay. Based on the BBN model, a decision-support tool, BBN Tunnel, was developed to assess delay considering the impacts of implementing alternative risk mitigation strategies. The tool developed in collaboration with a company was utilized in a tunnel project to test its usability in practice. The results demonstrated that BBN Tunnel and risk assessment method could be used to model interrelations between risk factors, construct a risk network, predict delay and help decision-makers formulate cost-effective risk mitigation strategies.

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85109373883&origin=inward
https://hdl.handle.net/11511/91355

Journal

Automation in Construction

DOI

https://doi.org/10.1016/j.autcon.2021.103819

Collections

Department of Civil Engineering, Article

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Citation Formats

G. C. Koseoglu Balta, İ. Dikmen Toker, and M. T. Birgönül, “Bayesian network based decision support for predicting and mitigating delay risk in TBM tunnel projects,” Automation in Construction, pp. 0–0, 2021, Accessed: 00, 2021. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85109373883&origin=inward.