Risk analysis during tunnel construction using Bayesian Networks: Porto Metro case study
Name
Einstein_Risk analysis.pdf
Size
4.41 MB
Format
Adobe PDF
Checksum (MD5)
74af9a603b8b6d0ae3f2b4869fd531ac
Author(s)
Sousa, Rita L.
Einstein, Herbert H.
Date Issued
August 2011
Journal
Tunnelling and Underground Space Technology
Publisher
Elsevier
Citation
Sousa, Rita L., and Herbert H. Einstein. “Risk Analysis During Tunnel Construction Using Bayesian Networks: Porto Metro Case Study.” Tunnelling and Underground Space Technology 27, no. 1 (January 2012): 86–100.
Version
Author's final manuscript
Abstract
This paper presents a methodology to systematically assess and manage the risks associated with tunnel construction. The methodology consists of combining a geologic prediction model that allows one to predict geology ahead of the tunnel construction, with a construction strategy decision model that allows one to choose amongst different construction strategies the one that leads to minimum risk. This model used tunnel boring machine performance data to relate to and predict geology. Both models are based on Bayesian Networks because of their ability to combine domain knowledge with data, encode dependencies among variables, and their ability to learn causal relationships. The combined geologic prediction–construction strategy decision model was applied to a case, the Porto Metro, in Portugal. The results of the geologic prediction model were in good agreement with the observed geology, and the results of the construction strategy decision support model were in good agreement with the construction methods used. Very significant is the ability of the model to predict changes in geology and consequently required changes in construction strategy. This risk assessment methodology provides a powerful tool with which planners and engineers can systematically assess and mitigate the inherent risks associated with tunnel construction.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.tust.2011.07.003
