Auto machine learning-based modelling and prediction of excavation-induced tunnel displacement
The influence of a deep excavation on existing shield tunnels nearby is a vital issue in tunnelling engineering. Whereas, there lacks robust methods to predict excavation-induced tunnel displacements. In this study, an auto machine learning (AutoML)-based approach is proposed to precisely solve the...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-08-01
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| Series: | Journal of Rock Mechanics and Geotechnical Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1674775522000786 |
| _version_ | 1811315896576114688 |
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| author | Dongmei Zhang Yiming Shen Zhongkai Huang Xiaochuang Xie |
| author_facet | Dongmei Zhang Yiming Shen Zhongkai Huang Xiaochuang Xie |
| author_sort | Dongmei Zhang |
| collection | DOAJ |
| description | The influence of a deep excavation on existing shield tunnels nearby is a vital issue in tunnelling engineering. Whereas, there lacks robust methods to predict excavation-induced tunnel displacements. In this study, an auto machine learning (AutoML)-based approach is proposed to precisely solve the issue. Seven input parameters are considered in the database covering two physical aspects, namely soil property, and spatial characteristics of the deep excavation. The 10-fold cross-validation method is employed to overcome the scarcity of data, and promote model's robustness. Six genetic algorithm (GA)-ML models are established as well for comparison. The results indicated that the proposed AutoML model is a comprehensive model that integrates efficiency and robustness. Importance analysis reveals that the ratio of the average shear strength to the vertical effective stress Eur/σv′, the excavation depth H, and the excavation width B are the most influential variables for the displacements. Finally, the AutoML model is further validated by practical engineering. The prediction results are in a good agreement with monitoring data, signifying that our model can be applied in real projects. |
| first_indexed | 2024-04-13T11:39:44Z |
| format | Article |
| id | doaj.art-5f58793203a543abb655090c01db5690 |
| institution | Directory Open Access Journal |
| issn | 1674-7755 |
| language | English |
| last_indexed | 2024-04-13T11:39:44Z |
| publishDate | 2022-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Rock Mechanics and Geotechnical Engineering |
| spelling | doaj.art-5f58793203a543abb655090c01db56902022-12-22T02:48:21ZengElsevierJournal of Rock Mechanics and Geotechnical Engineering1674-77552022-08-0114411001114Auto machine learning-based modelling and prediction of excavation-induced tunnel displacementDongmei Zhang0Yiming Shen1Zhongkai Huang2Xiaochuang Xie3Key Laboratory of Geotechnical and Underground Engineering, Ministry of Education, Tongji University, Shanghai, 200092, China; Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, Shanghai, 200092, China; Corresponding author. Key Laboratory of Geotechnical and Underground Engineering, Ministry of Education, Tongji University, Shanghai, 200092, China.Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, Shanghai, 200092, ChinaDepartment of Geotechnical Engineering, College of Civil Engineering, Tongji University, Shanghai, 200092, ChinaDepartment of Geotechnical Engineering, College of Civil Engineering, Tongji University, Shanghai, 200092, ChinaThe influence of a deep excavation on existing shield tunnels nearby is a vital issue in tunnelling engineering. Whereas, there lacks robust methods to predict excavation-induced tunnel displacements. In this study, an auto machine learning (AutoML)-based approach is proposed to precisely solve the issue. Seven input parameters are considered in the database covering two physical aspects, namely soil property, and spatial characteristics of the deep excavation. The 10-fold cross-validation method is employed to overcome the scarcity of data, and promote model's robustness. Six genetic algorithm (GA)-ML models are established as well for comparison. The results indicated that the proposed AutoML model is a comprehensive model that integrates efficiency and robustness. Importance analysis reveals that the ratio of the average shear strength to the vertical effective stress Eur/σv′, the excavation depth H, and the excavation width B are the most influential variables for the displacements. Finally, the AutoML model is further validated by practical engineering. The prediction results are in a good agreement with monitoring data, signifying that our model can be applied in real projects.http://www.sciencedirect.com/science/article/pii/S1674775522000786Soil–structure interactionAuto machine learning (AutoML)Displacement predictionRobust modelGeotechnical engineering |
| spellingShingle | Dongmei Zhang Yiming Shen Zhongkai Huang Xiaochuang Xie Auto machine learning-based modelling and prediction of excavation-induced tunnel displacement Journal of Rock Mechanics and Geotechnical Engineering Soil–structure interaction Auto machine learning (AutoML) Displacement prediction Robust model Geotechnical engineering |
| title | Auto machine learning-based modelling and prediction of excavation-induced tunnel displacement |
| title_full | Auto machine learning-based modelling and prediction of excavation-induced tunnel displacement |
| title_fullStr | Auto machine learning-based modelling and prediction of excavation-induced tunnel displacement |
| title_full_unstemmed | Auto machine learning-based modelling and prediction of excavation-induced tunnel displacement |
| title_short | Auto machine learning-based modelling and prediction of excavation-induced tunnel displacement |
| title_sort | auto machine learning based modelling and prediction of excavation induced tunnel displacement |
| topic | Soil–structure interaction Auto machine learning (AutoML) Displacement prediction Robust model Geotechnical engineering |
| url | http://www.sciencedirect.com/science/article/pii/S1674775522000786 |
| work_keys_str_mv | AT dongmeizhang automachinelearningbasedmodellingandpredictionofexcavationinducedtunneldisplacement AT yimingshen automachinelearningbasedmodellingandpredictionofexcavationinducedtunneldisplacement AT zhongkaihuang automachinelearningbasedmodellingandpredictionofexcavationinducedtunneldisplacement AT xiaochuangxie automachinelearningbasedmodellingandpredictionofexcavationinducedtunneldisplacement |