Machine Learning-Based 3D Soil Layer Reconstruction in Foundation Pit Engineering
In the construction of deep foundation pits, early warning measures are essential to reduce construction risks and prevent personnel injuries. In underground structure and pressure analysis, soil layer and support structure data are indispensable. Therefore, soil layer reconstruction serves as a cri...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/8/4078 |
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| author | Chenxi Zhang Nan Li Xiuping Dong Bin Liu Meilian Liu |
| author_facet | Chenxi Zhang Nan Li Xiuping Dong Bin Liu Meilian Liu |
| author_sort | Chenxi Zhang |
| collection | DOAJ |
| description | In the construction of deep foundation pits, early warning measures are essential to reduce construction risks and prevent personnel injuries. In underground structure and pressure analysis, soil layer and support structure data are indispensable. Therefore, soil layer reconstruction serves as a critical step, while sparse borehole data limit the accuracy of traditional reconstruction methods. This paper proposes a machine learning-based soil layer reconstruction method to address this issue. First, various types of borehole and soil layer data are generated by simulating the formation process of Earth’s soil layers, thereby providing sufficient training data. Subsequently, a coding algorithm is designed to extract soil layer features as inputs for the convolutional neural network. Finally, 3D meshing is performed on the soil layer generated from real boreholes, and soil model rendering is achieved through a voxel clustering algorithm. The algorithm achieved an accuracy rate of over 90% in tests and demonstrated excellent robustness. By applying this algorithm, we successfully reconstructed the soil layers at a typical foundation pit site in a Chinese city, validating its effectiveness in real-world scenarios and its potential for large-scale engineering applications. |
| format | Article |
| id | doaj-art-e415bc509ec94bb3aaa1420d52129f0c |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-e415bc509ec94bb3aaa1420d52129f0c2025-08-20T02:17:14ZengMDPI AGApplied Sciences2076-34172025-04-01158407810.3390/app15084078Machine Learning-Based 3D Soil Layer Reconstruction in Foundation Pit EngineeringChenxi Zhang0Nan Li1Xiuping Dong2Bin Liu3Meilian Liu4School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100142, ChinaSchool of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100142, ChinaSchool of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100142, ChinaSchool of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100142, ChinaSchool of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100142, ChinaIn the construction of deep foundation pits, early warning measures are essential to reduce construction risks and prevent personnel injuries. In underground structure and pressure analysis, soil layer and support structure data are indispensable. Therefore, soil layer reconstruction serves as a critical step, while sparse borehole data limit the accuracy of traditional reconstruction methods. This paper proposes a machine learning-based soil layer reconstruction method to address this issue. First, various types of borehole and soil layer data are generated by simulating the formation process of Earth’s soil layers, thereby providing sufficient training data. Subsequently, a coding algorithm is designed to extract soil layer features as inputs for the convolutional neural network. Finally, 3D meshing is performed on the soil layer generated from real boreholes, and soil model rendering is achieved through a voxel clustering algorithm. The algorithm achieved an accuracy rate of over 90% in tests and demonstrated excellent robustness. By applying this algorithm, we successfully reconstructed the soil layers at a typical foundation pit site in a Chinese city, validating its effectiveness in real-world scenarios and its potential for large-scale engineering applications.https://www.mdpi.com/2076-3417/15/8/4078soil layer visualizationsoil layer reconstructionsparse borehole codingCNNfoundation pit engineering |
| spellingShingle | Chenxi Zhang Nan Li Xiuping Dong Bin Liu Meilian Liu Machine Learning-Based 3D Soil Layer Reconstruction in Foundation Pit Engineering Applied Sciences soil layer visualization soil layer reconstruction sparse borehole coding CNN foundation pit engineering |
| title | Machine Learning-Based 3D Soil Layer Reconstruction in Foundation Pit Engineering |
| title_full | Machine Learning-Based 3D Soil Layer Reconstruction in Foundation Pit Engineering |
| title_fullStr | Machine Learning-Based 3D Soil Layer Reconstruction in Foundation Pit Engineering |
| title_full_unstemmed | Machine Learning-Based 3D Soil Layer Reconstruction in Foundation Pit Engineering |
| title_short | Machine Learning-Based 3D Soil Layer Reconstruction in Foundation Pit Engineering |
| title_sort | machine learning based 3d soil layer reconstruction in foundation pit engineering |
| topic | soil layer visualization soil layer reconstruction sparse borehole coding CNN foundation pit engineering |
| url | https://www.mdpi.com/2076-3417/15/8/4078 |
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