Risk analysis during tunnel construction using Bayesian Networks: Porto Metro case study
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...
| Main Authors: | , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Elsevier
2016
|
| Online Access: | http://hdl.handle.net/1721.1/101601 https://orcid.org/0000-0003-4074-4736 |
| _version_ | 1826200470073376768 |
|---|---|
| author | Sousa, Rita L. Einstein, Herbert H. |
| author2 | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Sousa, Rita L. Einstein, Herbert H. |
| author_sort | Sousa, Rita L. |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T11:36:59Z |
| format | Article |
| id | mit-1721.1/101601 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T11:36:59Z |
| publishDate | 2016 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | mit-1721.1/1016012022-10-01T04:49:36Z Risk analysis during tunnel construction using Bayesian Networks: Porto Metro case study Sousa, Rita L. Einstein, Herbert H. Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Einstein, Herbert H. Sousa, Rita L. Einstein, Herbert H. 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. Fundacao para a Ciencia e a Tecnologia (Doctoral Research Fellowship) 2016-03-04T16:37:56Z 2016-03-04T16:37:56Z 2011-08 2011-07 Article http://purl.org/eprint/type/JournalArticle 08867798 http://hdl.handle.net/1721.1/101601 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. https://orcid.org/0000-0003-4074-4736 en_US http://dx.doi.org/10.1016/j.tust.2011.07.003 Tunnelling and Underground Space Technology Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Elsevier Prof. Einstein |
| spellingShingle | Sousa, Rita L. Einstein, Herbert H. Risk analysis during tunnel construction using Bayesian Networks: Porto Metro case study |
| title | Risk analysis during tunnel construction using Bayesian Networks: Porto Metro case study |
| title_full | Risk analysis during tunnel construction using Bayesian Networks: Porto Metro case study |
| title_fullStr | Risk analysis during tunnel construction using Bayesian Networks: Porto Metro case study |
| title_full_unstemmed | Risk analysis during tunnel construction using Bayesian Networks: Porto Metro case study |
| title_short | Risk analysis during tunnel construction using Bayesian Networks: Porto Metro case study |
| title_sort | risk analysis during tunnel construction using bayesian networks porto metro case study |
| url | http://hdl.handle.net/1721.1/101601 https://orcid.org/0000-0003-4074-4736 |
| work_keys_str_mv | AT sousarital riskanalysisduringtunnelconstructionusingbayesiannetworksportometrocasestudy AT einsteinherberth riskanalysisduringtunnelconstructionusingbayesiannetworksportometrocasestudy |