Excavation-induced fault instability: a machine learning perspective
Excavation-induced fault instability has been known as a major barrier for underground engineering in deep rocks. A comprehensive understanding of unloading-induced stress changes on a pre-existing fault is a critical clue to reveal the mechanism of excavation-induced fault instability. Here we esta...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/178265 |
| _version_ | 1826120737854849024 |
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| author | Meng, Wenzhao Xu, Nuwen Zhao, Zhihong Wu, Wei |
| author2 | School of Civil and Environmental Engineering |
| author_facet | School of Civil and Environmental Engineering Meng, Wenzhao Xu, Nuwen Zhao, Zhihong Wu, Wei |
| author_sort | Meng, Wenzhao |
| collection | NTU |
| description | Excavation-induced fault instability has been known as a major barrier for underground engineering in deep rocks. A comprehensive understanding of unloading-induced stress changes on a pre-existing fault is a critical clue to reveal the mechanism of excavation-induced fault instability. Here we established a machine learning model based on eXtreme Gradient Boosting (XGBoost) to predict changes in normal stress, shear stress, and Coulomb failure stress along the fault due to tunnel excavation. We first created the training datasets based on discrete-element modeling and tested machine learning models to select a better performing model. We then conducted a relative importance analysis and showed that the horizontal stress on the model and the coordinates along the fault are two critical factors to predict the stress changes. We used the XGBoost model to further investigate the fault-slip rockburst during the construction of Jinping II Hydropower station and demonstrated the relationships between the stress changes and the failure locations. Finally, we discussed an interesting correlation between the stress changes (reduction-dominated and rotation-dominated) and the failure locations (initiation and termination) along the fault, which is crucial to understand the mechanism of excavation-induced fault instability and to forecast the fault failure during tunnel excavation. |
| first_indexed | 2024-10-01T05:21:21Z |
| format | Journal Article |
| id | ntu-10356/178265 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:21:21Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-10356/1782652024-06-10T02:47:32Z Excavation-induced fault instability: a machine learning perspective Meng, Wenzhao Xu, Nuwen Zhao, Zhihong Wu, Wei School of Civil and Environmental Engineering Engineering Tunnel excavation Fault instability Excavation-induced fault instability has been known as a major barrier for underground engineering in deep rocks. A comprehensive understanding of unloading-induced stress changes on a pre-existing fault is a critical clue to reveal the mechanism of excavation-induced fault instability. Here we established a machine learning model based on eXtreme Gradient Boosting (XGBoost) to predict changes in normal stress, shear stress, and Coulomb failure stress along the fault due to tunnel excavation. We first created the training datasets based on discrete-element modeling and tested machine learning models to select a better performing model. We then conducted a relative importance analysis and showed that the horizontal stress on the model and the coordinates along the fault are two critical factors to predict the stress changes. We used the XGBoost model to further investigate the fault-slip rockburst during the construction of Jinping II Hydropower station and demonstrated the relationships between the stress changes and the failure locations. Finally, we discussed an interesting correlation between the stress changes (reduction-dominated and rotation-dominated) and the failure locations (initiation and termination) along the fault, which is crucial to understand the mechanism of excavation-induced fault instability and to forecast the fault failure during tunnel excavation. National Research Foundation (NRF) This research is supported by National Research Foundation, Singapore under its Virtual Singapore R&D Programme (Award No. NRF2019VSG-GMS-001). 2024-06-10T02:47:32Z 2024-06-10T02:47:32Z 2024 Journal Article Meng, W., Xu, N., Zhao, Z. & Wu, W. (2024). Excavation-induced fault instability: a machine learning perspective. Rock Mechanics and Rock Engineering. https://dx.doi.org/10.1007/s00603-024-03817-6 0723-2632 https://hdl.handle.net/10356/178265 10.1007/s00603-024-03817-6 2-s2.0-85188086991 en NRF2019VSG-GMS-001 Rock Mechanics and Rock Engineering © 2024 The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature. All rights reserved. |
| spellingShingle | Engineering Tunnel excavation Fault instability Meng, Wenzhao Xu, Nuwen Zhao, Zhihong Wu, Wei Excavation-induced fault instability: a machine learning perspective |
| title | Excavation-induced fault instability: a machine learning perspective |
| title_full | Excavation-induced fault instability: a machine learning perspective |
| title_fullStr | Excavation-induced fault instability: a machine learning perspective |
| title_full_unstemmed | Excavation-induced fault instability: a machine learning perspective |
| title_short | Excavation-induced fault instability: a machine learning perspective |
| title_sort | excavation induced fault instability a machine learning perspective |
| topic | Engineering Tunnel excavation Fault instability |
| url | https://hdl.handle.net/10356/178265 |
| work_keys_str_mv | AT mengwenzhao excavationinducedfaultinstabilityamachinelearningperspective AT xunuwen excavationinducedfaultinstabilityamachinelearningperspective AT zhaozhihong excavationinducedfaultinstabilityamachinelearningperspective AT wuwei excavationinducedfaultinstabilityamachinelearningperspective |