Shield tunneling efficiency and stability enhancement based on interpretable machine learning and multi-objective optimization
Adequate control of shield machine parameters to ensure the safety and efficiency of shield construction is a difficult and complex problem. To address this problem, this paper proposes a hybrid intelligent optimization framework that combines interpretable machine learning, intelligent optimization...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-06-01
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| Series: | Underground Space |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2467967425000200 |
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| author | Wenli Liu Yang Chen Tianxiang Liu Wen Liu Jue Li Yangyang Chen |
| author_facet | Wenli Liu Yang Chen Tianxiang Liu Wen Liu Jue Li Yangyang Chen |
| author_sort | Wenli Liu |
| collection | DOAJ |
| description | Adequate control of shield machine parameters to ensure the safety and efficiency of shield construction is a difficult and complex problem. To address this problem, this paper proposes a hybrid intelligent optimization framework that combines interpretable machine learning, intelligent optimization algorithms, and multi-objective optimization and decision-making methods. The nonlinear relationship between the input parameters and ground settlement (GS) is fitted based on the light gradient boosting machine (LGBM), and the effect of the input parameters on GS is analysed based on SHapley additive exPlanation for further feature selection. Subsequently, the hyperparameters of LGBM were determined based on the sparrow search algorithm (SSA) to better fit the input–output relationship. On this basis, a multi-objective intelligent optimization model is established to solve the optimized operating parameters of shield machine by non-dominated sorting genetic algorithm II and technique for order preference by similarity to ideal solution to reduce GS and improve drilling efficiency. The results demonstrate that the SSA-LGBM model predicts GS with high accuracy, exhibiting an RMSE of 4.775, a VAF of 0.930 and an R2 of 0.931. These metrics collectively reflect the model’s excellent performance in prediction accuracy, ability to explain data variability, and control of prediction bias. The multi-objective optimization model is effective in optimizing two objectives, and the improvement can reach up to 39.38%; at the same time, the model has high scalability and can also be applied to three or more objectives. The intelligent optimization framework for shield construction parameters proposed in this paper can generate the optimal parameter combinations for shield machine manipulation, and provide reference and guidance when there are conflicting optimization objectives. |
| format | Article |
| id | doaj-art-a104cf463405482db44f115d929ca336 |
| institution | OA Journals |
| issn | 2467-9674 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Underground Space |
| spelling | doaj-art-a104cf463405482db44f115d929ca3362025-08-20T02:02:25ZengKeAi Communications Co., Ltd.Underground Space2467-96742025-06-012232033610.1016/j.undsp.2025.01.001Shield tunneling efficiency and stability enhancement based on interpretable machine learning and multi-objective optimizationWenli Liu0Yang Chen1Tianxiang Liu2Wen Liu3Jue Li4Yangyang Chen5School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China; Center of Technology Innovation for Digital Construction, Wuhan, Hubei 430074, ChinaSchool of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China; Center of Technology Innovation for Digital Construction, Wuhan, Hubei 430074, ChinaSchool of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China; Center of Technology Innovation for Digital Construction, Wuhan, Hubei 430074, ChinaCCCC Wuhan Zhixing International Engineering Consulting Co., Ltd., Wuhan, Hubei 430040, ChinaAcademy of International Law and Global Governance, Wuhan University, Wuhan, Hubei 430072, ChinaSchool of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China; Corresponding author.Adequate control of shield machine parameters to ensure the safety and efficiency of shield construction is a difficult and complex problem. To address this problem, this paper proposes a hybrid intelligent optimization framework that combines interpretable machine learning, intelligent optimization algorithms, and multi-objective optimization and decision-making methods. The nonlinear relationship between the input parameters and ground settlement (GS) is fitted based on the light gradient boosting machine (LGBM), and the effect of the input parameters on GS is analysed based on SHapley additive exPlanation for further feature selection. Subsequently, the hyperparameters of LGBM were determined based on the sparrow search algorithm (SSA) to better fit the input–output relationship. On this basis, a multi-objective intelligent optimization model is established to solve the optimized operating parameters of shield machine by non-dominated sorting genetic algorithm II and technique for order preference by similarity to ideal solution to reduce GS and improve drilling efficiency. The results demonstrate that the SSA-LGBM model predicts GS with high accuracy, exhibiting an RMSE of 4.775, a VAF of 0.930 and an R2 of 0.931. These metrics collectively reflect the model’s excellent performance in prediction accuracy, ability to explain data variability, and control of prediction bias. The multi-objective optimization model is effective in optimizing two objectives, and the improvement can reach up to 39.38%; at the same time, the model has high scalability and can also be applied to three or more objectives. The intelligent optimization framework for shield construction parameters proposed in this paper can generate the optimal parameter combinations for shield machine manipulation, and provide reference and guidance when there are conflicting optimization objectives.http://www.sciencedirect.com/science/article/pii/S2467967425000200Shield tunnelling machineInterpretable machine learningHyperparameter optimizationMulti-objective optimizationField penetration indexGround settlement |
| spellingShingle | Wenli Liu Yang Chen Tianxiang Liu Wen Liu Jue Li Yangyang Chen Shield tunneling efficiency and stability enhancement based on interpretable machine learning and multi-objective optimization Underground Space Shield tunnelling machine Interpretable machine learning Hyperparameter optimization Multi-objective optimization Field penetration index Ground settlement |
| title | Shield tunneling efficiency and stability enhancement based on interpretable machine learning and multi-objective optimization |
| title_full | Shield tunneling efficiency and stability enhancement based on interpretable machine learning and multi-objective optimization |
| title_fullStr | Shield tunneling efficiency and stability enhancement based on interpretable machine learning and multi-objective optimization |
| title_full_unstemmed | Shield tunneling efficiency and stability enhancement based on interpretable machine learning and multi-objective optimization |
| title_short | Shield tunneling efficiency and stability enhancement based on interpretable machine learning and multi-objective optimization |
| title_sort | shield tunneling efficiency and stability enhancement based on interpretable machine learning and multi objective optimization |
| topic | Shield tunnelling machine Interpretable machine learning Hyperparameter optimization Multi-objective optimization Field penetration index Ground settlement |
| url | http://www.sciencedirect.com/science/article/pii/S2467967425000200 |
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