Calculating Rock Joint Frequency in TBM Excavation Through Binocular Vision and Segmentation Techniques
In tunnel boring machine (TBM) construction, the strength and integrity of the surrounding rock are pivotal factors affecting operational safety and efficiency. The stability of the surrounding rock is particularly influenced by structural planes within the rock mass. Hence, rapidly and accurately a...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/adce/4515005 |
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| _version_ | 1839626649344147456 |
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| author | Jingwei Xu Haochen Sun Hongmei Wang Yaxu Wang Yan Zhu Peng Jiang |
| author_facet | Jingwei Xu Haochen Sun Hongmei Wang Yaxu Wang Yan Zhu Peng Jiang |
| author_sort | Jingwei Xu |
| collection | DOAJ |
| description | In tunnel boring machine (TBM) construction, the strength and integrity of the surrounding rock are pivotal factors affecting operational safety and efficiency. The stability of the surrounding rock is particularly influenced by structural planes within the rock mass. Hence, rapidly and accurately acquiring integrity information during construction is crucial for ensuring TBM safety and optimizing performance. This study introduces an innovative method integrating binocular vision for rapid distance measurement with image-level segmentation for rock joint detection. The method enhances the speed and precision of determining rock mass integrity parameters, specifically the frequency of rock joints, thereby providing reliable and efficient rock mechanics data for TBM operations. The binocular vision system captures original images and distance distributions, which are processed using a refined edge detection model for segmentation. This approach effectively identifies and characterizes diverse joint morphologies, enabling accurate calculations of rock joint frequency. Validation on a dataset comprising 30 groups demonstrated a prediction accuracy of 0.79, confirming the robustness and effectiveness of the method even under challenging conditions such as occlusion and water seepage. |
| format | Article |
| id | doaj-art-dd94c88f8aa64ce9b628bccd7efc0d25 |
| institution | Matheson Library |
| issn | 1687-8094 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-dd94c88f8aa64ce9b628bccd7efc0d252025-07-17T05:00:01ZengWileyAdvances in Civil Engineering1687-80942025-01-01202510.1155/adce/4515005Calculating Rock Joint Frequency in TBM Excavation Through Binocular Vision and Segmentation TechniquesJingwei Xu0Haochen Sun1Hongmei Wang2Yaxu Wang3Yan Zhu4Peng Jiang5Jinan Heavy Industries Group Co., Ltd.School of Computer Science and TechnologyJinan Heavy Industries Group Co., Ltd.School of Qilu TransportationInstitute of Geotechnical and Underground EngineeringSchool of Qilu TransportationIn tunnel boring machine (TBM) construction, the strength and integrity of the surrounding rock are pivotal factors affecting operational safety and efficiency. The stability of the surrounding rock is particularly influenced by structural planes within the rock mass. Hence, rapidly and accurately acquiring integrity information during construction is crucial for ensuring TBM safety and optimizing performance. This study introduces an innovative method integrating binocular vision for rapid distance measurement with image-level segmentation for rock joint detection. The method enhances the speed and precision of determining rock mass integrity parameters, specifically the frequency of rock joints, thereby providing reliable and efficient rock mechanics data for TBM operations. The binocular vision system captures original images and distance distributions, which are processed using a refined edge detection model for segmentation. This approach effectively identifies and characterizes diverse joint morphologies, enabling accurate calculations of rock joint frequency. Validation on a dataset comprising 30 groups demonstrated a prediction accuracy of 0.79, confirming the robustness and effectiveness of the method even under challenging conditions such as occlusion and water seepage.http://dx.doi.org/10.1155/adce/4515005 |
| spellingShingle | Jingwei Xu Haochen Sun Hongmei Wang Yaxu Wang Yan Zhu Peng Jiang Calculating Rock Joint Frequency in TBM Excavation Through Binocular Vision and Segmentation Techniques Advances in Civil Engineering |
| title | Calculating Rock Joint Frequency in TBM Excavation Through Binocular Vision and Segmentation Techniques |
| title_full | Calculating Rock Joint Frequency in TBM Excavation Through Binocular Vision and Segmentation Techniques |
| title_fullStr | Calculating Rock Joint Frequency in TBM Excavation Through Binocular Vision and Segmentation Techniques |
| title_full_unstemmed | Calculating Rock Joint Frequency in TBM Excavation Through Binocular Vision and Segmentation Techniques |
| title_short | Calculating Rock Joint Frequency in TBM Excavation Through Binocular Vision and Segmentation Techniques |
| title_sort | calculating rock joint frequency in tbm excavation through binocular vision and segmentation techniques |
| url | http://dx.doi.org/10.1155/adce/4515005 |
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