Reliable prediction for TBM energy consumption during tunnel excavation: A novel technique balancing explainability and performance
Recently, AI-based models have been applied to accurately estimate tunnel boring machine (TBM) energy consumption. Although data-driven models exhibit strong predictive capabilities, their outputs derived from “black box” processes are challenging to interpret and generalize. Consequently, this stud...
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| Format: | Article |
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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/S2467967424001338 |
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| author | Wenli Liu Yafei Qi Fenghua Liu |
| author_facet | Wenli Liu Yafei Qi Fenghua Liu |
| author_sort | Wenli Liu |
| collection | DOAJ |
| description | Recently, AI-based models have been applied to accurately estimate tunnel boring machine (TBM) energy consumption. Although data-driven models exhibit strong predictive capabilities, their outputs derived from “black box” processes are challenging to interpret and generalize. Consequently, this study develops an XGB_MOFS model that cooperates extreme gradient boosting (XGBoost) and multi-objective feature selection (MOFS) to improve the accuracy and explainability of energy consumption prediction. The XGB_MOFS model includes: (1) a causal inference framework to identify the causal relationships among influential factors, and (2) a MOFS approach to balance predictive performance and explainability. Two case studies are carried out to verify the proposed method. Results show that XGB_MOFS achieves a high degree of accuracy and robustness in energy consumption prediction. The XGB_MOFS model, balancing accuracy with explainability, serves as an effective and feasible tool for regulating TBM energy consumption. |
| format | Article |
| id | doaj-art-38d45f3de9bb4078918c8f1feebb69f8 |
| 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-38d45f3de9bb4078918c8f1feebb69f82025-08-20T02:02:25ZengKeAi Communications Co., Ltd.Underground Space2467-96742025-06-0122779510.1016/j.undsp.2024.09.004Reliable prediction for TBM energy consumption during tunnel excavation: A novel technique balancing explainability and performanceWenli Liu0Yafei Qi1Fenghua Liu2School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China; National Center of Technology Innovation for Digital Construction, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China; National Center of Technology Innovation for Digital Construction, Huazhong University of Science and Technology, Wuhan 430074, ChinaCorresponding author at: School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.; School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China; National Center of Technology Innovation for Digital Construction, Huazhong University of Science and Technology, Wuhan 430074, ChinaRecently, AI-based models have been applied to accurately estimate tunnel boring machine (TBM) energy consumption. Although data-driven models exhibit strong predictive capabilities, their outputs derived from “black box” processes are challenging to interpret and generalize. Consequently, this study develops an XGB_MOFS model that cooperates extreme gradient boosting (XGBoost) and multi-objective feature selection (MOFS) to improve the accuracy and explainability of energy consumption prediction. The XGB_MOFS model includes: (1) a causal inference framework to identify the causal relationships among influential factors, and (2) a MOFS approach to balance predictive performance and explainability. Two case studies are carried out to verify the proposed method. Results show that XGB_MOFS achieves a high degree of accuracy and robustness in energy consumption prediction. The XGB_MOFS model, balancing accuracy with explainability, serves as an effective and feasible tool for regulating TBM energy consumption.http://www.sciencedirect.com/science/article/pii/S2467967424001338Machine learningMulti-objective feature selectionExplainabilityEnergy consumptionShield tunneling |
| spellingShingle | Wenli Liu Yafei Qi Fenghua Liu Reliable prediction for TBM energy consumption during tunnel excavation: A novel technique balancing explainability and performance Underground Space Machine learning Multi-objective feature selection Explainability Energy consumption Shield tunneling |
| title | Reliable prediction for TBM energy consumption during tunnel excavation: A novel technique balancing explainability and performance |
| title_full | Reliable prediction for TBM energy consumption during tunnel excavation: A novel technique balancing explainability and performance |
| title_fullStr | Reliable prediction for TBM energy consumption during tunnel excavation: A novel technique balancing explainability and performance |
| title_full_unstemmed | Reliable prediction for TBM energy consumption during tunnel excavation: A novel technique balancing explainability and performance |
| title_short | Reliable prediction for TBM energy consumption during tunnel excavation: A novel technique balancing explainability and performance |
| title_sort | reliable prediction for tbm energy consumption during tunnel excavation a novel technique balancing explainability and performance |
| topic | Machine learning Multi-objective feature selection Explainability Energy consumption Shield tunneling |
| url | http://www.sciencedirect.com/science/article/pii/S2467967424001338 |
| work_keys_str_mv | AT wenliliu reliablepredictionfortbmenergyconsumptionduringtunnelexcavationanoveltechniquebalancingexplainabilityandperformance AT yafeiqi reliablepredictionfortbmenergyconsumptionduringtunnelexcavationanoveltechniquebalancingexplainabilityandperformance AT fenghualiu reliablepredictionfortbmenergyconsumptionduringtunnelexcavationanoveltechniquebalancingexplainabilityandperformance |