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

Koseoglu Balta G., DİKMEN TOKER İ., BİRGÖNÜL M. T.

Automation in Construction, vol.129, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 129
  • Publication Date: 2021
  • Doi Number: 10.1016/j.autcon.2021.103819
  • Journal Name: Automation in Construction
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Communication Abstracts, ICONDA Bibliographic, INSPEC, Metadex, Civil Engineering Abstracts
  • Keywords: Delay risk assessment, Risk management, Decision support, Bayesian belief networks, Tunnels, SCHEDULE RISK, FAULT-TREE, CONSTRUCTION, MANAGEMENT, SYSTEM, MODEL, VALIDATION, KNOWLEDGE
  • Middle East Technical University Affiliated: Yes


© 2018Tunnel 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.