Prediction of Surface Settlement Induced by Large-Diameter Shield Tunneling Based on Machine-Learning Algorithms
The accurate prediction of surface settlement caused by large-diameter shield tunneling is crucial for the safety of the tunnel environment. However, due to the complexity and uncertainty of the rock-machine interaction and groundwater variation, it is difficult to predict the settlement by developi...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Geofluids |
| Online Access: | http://dx.doi.org/10.1155/2022/4174768 |
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| _version_ | 1850165665136115712 |
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| author | Chao Li Jinhui Li Zhongqi Shi Li Li Mingxiong Li Dianqi Jin Guo Dong |
| author_facet | Chao Li Jinhui Li Zhongqi Shi Li Li Mingxiong Li Dianqi Jin Guo Dong |
| author_sort | Chao Li |
| collection | DOAJ |
| description | The accurate prediction of surface settlement caused by large-diameter shield tunneling is crucial for the safety of the tunnel environment. However, due to the complexity and uncertainty of the rock-machine interaction and groundwater variation, it is difficult to predict the settlement by developing traditional theoretical methods. Recently, a big number of data obtained from the Chunfeng shield tunnel in China provides the possibility to predict the settlement using machine-learning methods. In this study, the equipment parameters, the geological parameters, and the monitored settlements are used to establish the models. Three machine-learning algorithms (i.e., long-short-term memory (LSTM), random forest (RF), and gated recurrent unit (GRU)) are used to predict the surface settlement. Three indicators, mean absolute error (MAE), accuracy (ACC), and coefficient of determination (R2), are selected to evaluate the prediction performance. Results demonstrated that the filtering and selection of model parameters is vitally important to the accuracy of model prediction. Among the three machine-learning algorithms, the LSTM algorithm gives the best accuracy in predicting the maximum surface settlement and can effectively predict the settlement development in different strata. |
| format | Article |
| id | doaj-art-ee67aad90fce4576aaa874ab9873a2c1 |
| institution | OA Journals |
| issn | 1468-8123 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geofluids |
| spelling | doaj-art-ee67aad90fce4576aaa874ab9873a2c12025-08-20T02:21:41ZengWileyGeofluids1468-81232022-01-01202210.1155/2022/4174768Prediction of Surface Settlement Induced by Large-Diameter Shield Tunneling Based on Machine-Learning AlgorithmsChao Li0Jinhui Li1Zhongqi Shi2Li Li3Mingxiong Li4Dianqi Jin5Guo Dong6Department of Civil and Environmental EngineeringDepartment of Civil and Environmental EngineeringShenzhen Urban Public Safety and Technology InstituteShenzhen Urban Public Safety and Technology InstituteDepartment of Civil and Environmental EngineeringShenzhen Urban Public Safety and Technology InstituteDepartment of Civil and Environmental EngineeringThe accurate prediction of surface settlement caused by large-diameter shield tunneling is crucial for the safety of the tunnel environment. However, due to the complexity and uncertainty of the rock-machine interaction and groundwater variation, it is difficult to predict the settlement by developing traditional theoretical methods. Recently, a big number of data obtained from the Chunfeng shield tunnel in China provides the possibility to predict the settlement using machine-learning methods. In this study, the equipment parameters, the geological parameters, and the monitored settlements are used to establish the models. Three machine-learning algorithms (i.e., long-short-term memory (LSTM), random forest (RF), and gated recurrent unit (GRU)) are used to predict the surface settlement. Three indicators, mean absolute error (MAE), accuracy (ACC), and coefficient of determination (R2), are selected to evaluate the prediction performance. Results demonstrated that the filtering and selection of model parameters is vitally important to the accuracy of model prediction. Among the three machine-learning algorithms, the LSTM algorithm gives the best accuracy in predicting the maximum surface settlement and can effectively predict the settlement development in different strata.http://dx.doi.org/10.1155/2022/4174768 |
| spellingShingle | Chao Li Jinhui Li Zhongqi Shi Li Li Mingxiong Li Dianqi Jin Guo Dong Prediction of Surface Settlement Induced by Large-Diameter Shield Tunneling Based on Machine-Learning Algorithms Geofluids |
| title | Prediction of Surface Settlement Induced by Large-Diameter Shield Tunneling Based on Machine-Learning Algorithms |
| title_full | Prediction of Surface Settlement Induced by Large-Diameter Shield Tunneling Based on Machine-Learning Algorithms |
| title_fullStr | Prediction of Surface Settlement Induced by Large-Diameter Shield Tunneling Based on Machine-Learning Algorithms |
| title_full_unstemmed | Prediction of Surface Settlement Induced by Large-Diameter Shield Tunneling Based on Machine-Learning Algorithms |
| title_short | Prediction of Surface Settlement Induced by Large-Diameter Shield Tunneling Based on Machine-Learning Algorithms |
| title_sort | prediction of surface settlement induced by large diameter shield tunneling based on machine learning algorithms |
| url | http://dx.doi.org/10.1155/2022/4174768 |
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