Analytical Graphical Approach for Predicting Ground Conditions in TBM-based Tunneling Construction
The present master's thesis addresses the use of Artificial Intelligence (AI) and Machine Learning (ML) algorithms to predict geology based on Tunnel Boring Machine (TBM) data. The use of mechanized tunneling has become frequent over the last decade, and their performance is critical for projec...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2024
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| Online Access: | https://hdl.handle.net/1721.1/153322 https://orcid.org/0000-0003-4504-2135 |
| _version_ | 1826201268658372608 |
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| author | Goncalves Klink, Beatriz |
| author2 | Einstein, Herbert H. |
| author_facet | Einstein, Herbert H. Goncalves Klink, Beatriz |
| author_sort | Goncalves Klink, Beatriz |
| collection | MIT |
| description | The present master's thesis addresses the use of Artificial Intelligence (AI) and Machine Learning (ML) algorithms to predict geology based on Tunnel Boring Machine (TBM) data. The use of mechanized tunneling has become frequent over the last decade, and their performance is critical for project management and safety. Numerical simulation methods have become prevalent in predicting TBM performance metrics, and the use of AI/ML techniques for prescient applications using TBM-generated data has become ubiquitous. The current research aims to propose an exploratory look into the correlation between specific TBM parameters and ground conditions. The methodology seeks to classify rings based on three main ground classes: rock, soil, and mixed, through the observation of clear patterns, found to be representative of these ground classes, which are demonstrated. A techno-economic assessment of the current use of AI/ML tools for geology prediction in TBM-based tunneling construction, is also presented, analyzing both the potential and shortcomings of the technology. For the purpose of the study, the Porto Metro project (Portugal) is introduced, used as a case study for the proposed methodology. As the mining and drilling market is projected to almost double from 2020-2030, and with the increasing use of TBMs, improving ground condition prediction is paramount to the advancement of tunneling automation efforts. The present thesis aims to further develop the field and open dialogue on the use and effectiveness of using purely AI/ML modelling methods for this application. |
| first_indexed | 2024-09-23T11:48:57Z |
| format | Thesis |
| id | mit-1721.1/153322 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T11:48:57Z |
| publishDate | 2024 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1533222024-01-17T03:37:24Z Analytical Graphical Approach for Predicting Ground Conditions in TBM-based Tunneling Construction Goncalves Klink, Beatriz Einstein, Herbert H. Massachusetts Institute of Technology. Department of Civil and Environmental Engineering The present master's thesis addresses the use of Artificial Intelligence (AI) and Machine Learning (ML) algorithms to predict geology based on Tunnel Boring Machine (TBM) data. The use of mechanized tunneling has become frequent over the last decade, and their performance is critical for project management and safety. Numerical simulation methods have become prevalent in predicting TBM performance metrics, and the use of AI/ML techniques for prescient applications using TBM-generated data has become ubiquitous. The current research aims to propose an exploratory look into the correlation between specific TBM parameters and ground conditions. The methodology seeks to classify rings based on three main ground classes: rock, soil, and mixed, through the observation of clear patterns, found to be representative of these ground classes, which are demonstrated. A techno-economic assessment of the current use of AI/ML tools for geology prediction in TBM-based tunneling construction, is also presented, analyzing both the potential and shortcomings of the technology. For the purpose of the study, the Porto Metro project (Portugal) is introduced, used as a case study for the proposed methodology. As the mining and drilling market is projected to almost double from 2020-2030, and with the increasing use of TBMs, improving ground condition prediction is paramount to the advancement of tunneling automation efforts. The present thesis aims to further develop the field and open dialogue on the use and effectiveness of using purely AI/ML modelling methods for this application. S.M. 2024-01-16T21:51:01Z 2024-01-16T21:51:01Z 2023-06 2023-06-22T14:49:37.082Z Thesis https://hdl.handle.net/1721.1/153322 https://orcid.org/0000-0003-4504-2135 CC0 - Public Domain Copyright Public Domain https://creativecommons.org/publicdomain/zero/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Goncalves Klink, Beatriz Analytical Graphical Approach for Predicting Ground Conditions in TBM-based Tunneling Construction |
| title | Analytical Graphical Approach for Predicting Ground Conditions in TBM-based Tunneling Construction |
| title_full | Analytical Graphical Approach for Predicting Ground Conditions in TBM-based Tunneling Construction |
| title_fullStr | Analytical Graphical Approach for Predicting Ground Conditions in TBM-based Tunneling Construction |
| title_full_unstemmed | Analytical Graphical Approach for Predicting Ground Conditions in TBM-based Tunneling Construction |
| title_short | Analytical Graphical Approach for Predicting Ground Conditions in TBM-based Tunneling Construction |
| title_sort | analytical graphical approach for predicting ground conditions in tbm based tunneling construction |
| url | https://hdl.handle.net/1721.1/153322 https://orcid.org/0000-0003-4504-2135 |
| work_keys_str_mv | AT goncalvesklinkbeatriz analyticalgraphicalapproachforpredictinggroundconditionsintbmbasedtunnelingconstruction |