Data-knowledge hybrid driven intelligent prediction method of tunnel excavation profiles geometric deformation
Applying digital twin technology to the tunnel excavation process enables intelligent tunnel construction, which is critical for ensuring construction safety and advancing intelligent building methods. However, the complex construction environment, involving numerous environmental factors and dynami...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2459317 |
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| author | Ya Hu Jingyi Lu Jun Zhu Huixin Zhang Ying Ren Jianlin Wu Jianbo Lai Heng Zhang Hongyue Zhao Xiang Zeng |
| author_facet | Ya Hu Jingyi Lu Jun Zhu Huixin Zhang Ying Ren Jianlin Wu Jianbo Lai Heng Zhang Hongyue Zhao Xiang Zeng |
| author_sort | Ya Hu |
| collection | DOAJ |
| description | Applying digital twin technology to the tunnel excavation process enables intelligent tunnel construction, which is critical for ensuring construction safety and advancing intelligent building methods. However, the complex construction environment, involving numerous environmental factors and dynamic changes, poses challenges for traditional deformation prediction and analysis methods. These methods fail to systematically integrate the elements of the excavation scene to achieve accurate prediction. Therefore, this paper proposes a data-knowledge hybrid driven intelligent prediction method of tunnel excavation profile geometric deformation. A dynamic data-driven tunnel excavation knowledge extraction method is designed, along with the construction of a multi-process coupled tunnel excavation knowledge base. Additionally, a deep learning-based excavation profile deformation prediction model is developed, integrating the data-knowledge hybrid driven method to improve prediction performance. The experimental results demonstrate that the method proposed in this paper can significantly integrate and analyse the deformation elements of the tunnel excavation scene. It achieves intelligent prediction of the tunnel excavation profile geometric deformation, with reductions of 57.46%, 76.00%, and 53.12% in MAE, MSE, and RMSE, respectively, compared with the traditional prediction model. This method effectively enhances the deformation prediction accuracy of the tunnel excavation profile twin model. |
| format | Article |
| id | doaj-art-3e985b905467413ab22d7fbba21423a0 |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-3e985b905467413ab22d7fbba21423a02025-02-06T02:08:49ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-12-0118110.1080/17538947.2025.2459317Data-knowledge hybrid driven intelligent prediction method of tunnel excavation profiles geometric deformationYa Hu0Jingyi Lu1Jun Zhu2Huixin Zhang3Ying Ren4Jianlin Wu5Jianbo Lai6Heng Zhang7Hongyue Zhao8Xiang Zeng9Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu, People’s Republic of ChinaFaculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu, People’s Republic of ChinaFaculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu, People’s Republic of ChinaCollege of Physics and Engineering, Chengdu Normal University, Chengdu, People’s Republic of ChinaFaculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu, People’s Republic of ChinaFaculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu, People’s Republic of ChinaSchool of Emergency Management, Chengdu University, Chengdu, People’s Republic of ChinaChina Railway Design Corporation, Tianjin, People’s Republic of ChinaBeijing Tianjin Intercity Railway Investment Co., Ltd, Beijing, People’s Republic of ChinaFaculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu, People’s Republic of ChinaApplying digital twin technology to the tunnel excavation process enables intelligent tunnel construction, which is critical for ensuring construction safety and advancing intelligent building methods. However, the complex construction environment, involving numerous environmental factors and dynamic changes, poses challenges for traditional deformation prediction and analysis methods. These methods fail to systematically integrate the elements of the excavation scene to achieve accurate prediction. Therefore, this paper proposes a data-knowledge hybrid driven intelligent prediction method of tunnel excavation profile geometric deformation. A dynamic data-driven tunnel excavation knowledge extraction method is designed, along with the construction of a multi-process coupled tunnel excavation knowledge base. Additionally, a deep learning-based excavation profile deformation prediction model is developed, integrating the data-knowledge hybrid driven method to improve prediction performance. The experimental results demonstrate that the method proposed in this paper can significantly integrate and analyse the deformation elements of the tunnel excavation scene. It achieves intelligent prediction of the tunnel excavation profile geometric deformation, with reductions of 57.46%, 76.00%, and 53.12% in MAE, MSE, and RMSE, respectively, compared with the traditional prediction model. This method effectively enhances the deformation prediction accuracy of the tunnel excavation profile twin model.https://www.tandfonline.com/doi/10.1080/17538947.2025.2459317Tunnel excavation profilegeometry deformationintelligent predictiondata-knowledge hybrid driven |
| spellingShingle | Ya Hu Jingyi Lu Jun Zhu Huixin Zhang Ying Ren Jianlin Wu Jianbo Lai Heng Zhang Hongyue Zhao Xiang Zeng Data-knowledge hybrid driven intelligent prediction method of tunnel excavation profiles geometric deformation International Journal of Digital Earth Tunnel excavation profile geometry deformation intelligent prediction data-knowledge hybrid driven |
| title | Data-knowledge hybrid driven intelligent prediction method of tunnel excavation profiles geometric deformation |
| title_full | Data-knowledge hybrid driven intelligent prediction method of tunnel excavation profiles geometric deformation |
| title_fullStr | Data-knowledge hybrid driven intelligent prediction method of tunnel excavation profiles geometric deformation |
| title_full_unstemmed | Data-knowledge hybrid driven intelligent prediction method of tunnel excavation profiles geometric deformation |
| title_short | Data-knowledge hybrid driven intelligent prediction method of tunnel excavation profiles geometric deformation |
| title_sort | data knowledge hybrid driven intelligent prediction method of tunnel excavation profiles geometric deformation |
| topic | Tunnel excavation profile geometry deformation intelligent prediction data-knowledge hybrid driven |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2025.2459317 |
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