TIM-FEM-ML synthetic technology for longitudinal optimization of tunnel excavated in the interlayered rock mass
The layout of underground engineering objects significantly influences the stability of the surrounding rock mass and construction safety. Despite advancements toward intellectualization and informatization in design optimization and safety assessments, mechanical analysis-based engineering computat...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-08-01
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| Series: | Underground Space |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2467967425000480 |
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| author | Hui Li Weizhong Chen Xiaoyun Shu Xianjun Tan Qun Sui |
| author_facet | Hui Li Weizhong Chen Xiaoyun Shu Xianjun Tan Qun Sui |
| author_sort | Hui Li |
| collection | DOAJ |
| description | The layout of underground engineering objects significantly influences the stability of the surrounding rock mass and construction safety. Despite advancements toward intellectualization and informatization in design optimization and safety assessments, mechanical analysis-based engineering computations still face certain impediments. Consequently, this paper proposes a comprehensive framework integrating tunnel information modelling (TIM), finite element method (FEM) and machine learning (ML) technology to optimize the tunnel longitudinal orientation. It also delves into the specifics of addressing the challenges associated with each technology. The framework encompasses three phases: parametric modelling based on TIM, automatic numerical simulation based on FEM, and intelligent optimization leveraging ML. Initially, geometric models of the geological formations and engineering structures are constructed on the TIM platform. Subsequently, data conversion is facilitated through the proposed transformation interface. Python codes are programmed to realize automatic processing of numerical simulation and results are extracted to the ML algorithm for the prediction model. An optimization algorithm is implanted in the numerical stream file to retrieve the optimal relative intersection angle between the tunnel axis and the trend of rocks. A case study is conducted to evaluate the feasibility of the proposed framework. Results demonstrate a substantial improvement in design and optimization accuracy and efficiency. This framework holds immense potential to propel the intellectualization and informatization of underground engineering. |
| format | Article |
| id | doaj-art-a097d0a3261940ea915bf8e922ab6429 |
| institution | Kabale University |
| issn | 2467-9674 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Underground Space |
| spelling | doaj-art-a097d0a3261940ea915bf8e922ab64292025-08-20T03:58:14ZengKeAi Communications Co., Ltd.Underground Space2467-96742025-08-012332734210.1016/j.undsp.2025.03.001TIM-FEM-ML synthetic technology for longitudinal optimization of tunnel excavated in the interlayered rock massHui Li0Weizhong Chen1Xiaoyun Shu2Xianjun Tan3Qun Sui4State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, ChinaState Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China; Corresponding author.State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China; College of Civil and Transportation Engineering, Hohai University, Nanjing 210024, ChinaState Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, ChinaSchool of Civil and Transportation Engineering, Ningbo University of Technology, Ningbo 315211, ChinaThe layout of underground engineering objects significantly influences the stability of the surrounding rock mass and construction safety. Despite advancements toward intellectualization and informatization in design optimization and safety assessments, mechanical analysis-based engineering computations still face certain impediments. Consequently, this paper proposes a comprehensive framework integrating tunnel information modelling (TIM), finite element method (FEM) and machine learning (ML) technology to optimize the tunnel longitudinal orientation. It also delves into the specifics of addressing the challenges associated with each technology. The framework encompasses three phases: parametric modelling based on TIM, automatic numerical simulation based on FEM, and intelligent optimization leveraging ML. Initially, geometric models of the geological formations and engineering structures are constructed on the TIM platform. Subsequently, data conversion is facilitated through the proposed transformation interface. Python codes are programmed to realize automatic processing of numerical simulation and results are extracted to the ML algorithm for the prediction model. An optimization algorithm is implanted in the numerical stream file to retrieve the optimal relative intersection angle between the tunnel axis and the trend of rocks. A case study is conducted to evaluate the feasibility of the proposed framework. Results demonstrate a substantial improvement in design and optimization accuracy and efficiency. This framework holds immense potential to propel the intellectualization and informatization of underground engineering.http://www.sciencedirect.com/science/article/pii/S2467967425000480Underground engineeringTunnel information modelingMachine learningDesign optimizationLayered rock mass |
| spellingShingle | Hui Li Weizhong Chen Xiaoyun Shu Xianjun Tan Qun Sui TIM-FEM-ML synthetic technology for longitudinal optimization of tunnel excavated in the interlayered rock mass Underground Space Underground engineering Tunnel information modeling Machine learning Design optimization Layered rock mass |
| title | TIM-FEM-ML synthetic technology for longitudinal optimization of tunnel excavated in the interlayered rock mass |
| title_full | TIM-FEM-ML synthetic technology for longitudinal optimization of tunnel excavated in the interlayered rock mass |
| title_fullStr | TIM-FEM-ML synthetic technology for longitudinal optimization of tunnel excavated in the interlayered rock mass |
| title_full_unstemmed | TIM-FEM-ML synthetic technology for longitudinal optimization of tunnel excavated in the interlayered rock mass |
| title_short | TIM-FEM-ML synthetic technology for longitudinal optimization of tunnel excavated in the interlayered rock mass |
| title_sort | tim fem ml synthetic technology for longitudinal optimization of tunnel excavated in the interlayered rock mass |
| topic | Underground engineering Tunnel information modeling Machine learning Design optimization Layered rock mass |
| url | http://www.sciencedirect.com/science/article/pii/S2467967425000480 |
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