Improvement of photogrammetric joint roughness coefficient value by integrating automatic shooting parameter selection and composite error model
In order to improve the accuracy of the photogrammetric joint roughness coefficient (JRC) value, the present study proposed a novel method combining an autonomous shooting parameter selection algorithm with a composite error model. Firstly, according to the depth map-based photogrammetric theory, th...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Journal of Rock Mechanics and Geotechnical Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1674775524000969 |
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| author | Qinzheng Yang Ang Li Feng Dai Zhen Cui Hongtian Wang |
| author_facet | Qinzheng Yang Ang Li Feng Dai Zhen Cui Hongtian Wang |
| author_sort | Qinzheng Yang |
| collection | DOAJ |
| description | In order to improve the accuracy of the photogrammetric joint roughness coefficient (JRC) value, the present study proposed a novel method combining an autonomous shooting parameter selection algorithm with a composite error model. Firstly, according to the depth map-based photogrammetric theory, the estimation of JRC from a three-dimensional (3D) digital surface model of rock discontinuities was presented. Secondly, an automatic shooting parameter selection algorithm was novelly proposed to establish the 3D model dataset of rock discontinuities with varying shooting parameters and target sizes. Meanwhile, the photogrammetric tests were performed with custom-built equipment capable of adjusting baseline lengths, and a total of 36 sets of JRC data was gathered via a combination of laboratory and field tests. Then, by combining the theory of point cloud coordinate computation error with the equation of JRC calculation, a composite error model controlled by the shooting parameters was proposed. This newly proposed model was validated via the 3D model dataset, demonstrating the capability to correct initially obtained JRC values solely based on shooting parameters. Furthermore, the implementation of this correction can significantly reduce errors in JRC values obtained via photographic measurement. Subsequently, our proposed error model was integrated into the shooting parameter selection algorithm, thus improving the rationality and convenience of selecting suitable shooting parameter combinations when dealing with target rock masses with different sizes. Moreover, the optimal combination of three shooting parameters was offered. JRC values resulting from various combinations of shooting parameters were verified by comparing them with 3D laser scan data. Finally, the application scope and limitations of the newly proposed approach were further addressed. |
| format | Article |
| id | doaj-art-26d7cd0613d044eda23c26ab12f4558c |
| institution | Kabale University |
| issn | 1674-7755 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Rock Mechanics and Geotechnical Engineering |
| spelling | doaj-art-26d7cd0613d044eda23c26ab12f4558c2025-01-17T04:49:06ZengElsevierJournal of Rock Mechanics and Geotechnical Engineering1674-77552025-01-01171200219Improvement of photogrammetric joint roughness coefficient value by integrating automatic shooting parameter selection and composite error modelQinzheng Yang0Ang Li1Feng Dai2Zhen Cui3Hongtian Wang4School of Highway, Chang'an University, Xi'an, 710064, ChinaSchool of Highway, Chang'an University, Xi'an, 710064, China; Corresponding author.State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu, 610065, ChinaState Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan, 430071, ChinaSchool of Highway, Chang'an University, Xi'an, 710064, ChinaIn order to improve the accuracy of the photogrammetric joint roughness coefficient (JRC) value, the present study proposed a novel method combining an autonomous shooting parameter selection algorithm with a composite error model. Firstly, according to the depth map-based photogrammetric theory, the estimation of JRC from a three-dimensional (3D) digital surface model of rock discontinuities was presented. Secondly, an automatic shooting parameter selection algorithm was novelly proposed to establish the 3D model dataset of rock discontinuities with varying shooting parameters and target sizes. Meanwhile, the photogrammetric tests were performed with custom-built equipment capable of adjusting baseline lengths, and a total of 36 sets of JRC data was gathered via a combination of laboratory and field tests. Then, by combining the theory of point cloud coordinate computation error with the equation of JRC calculation, a composite error model controlled by the shooting parameters was proposed. This newly proposed model was validated via the 3D model dataset, demonstrating the capability to correct initially obtained JRC values solely based on shooting parameters. Furthermore, the implementation of this correction can significantly reduce errors in JRC values obtained via photographic measurement. Subsequently, our proposed error model was integrated into the shooting parameter selection algorithm, thus improving the rationality and convenience of selecting suitable shooting parameter combinations when dealing with target rock masses with different sizes. Moreover, the optimal combination of three shooting parameters was offered. JRC values resulting from various combinations of shooting parameters were verified by comparing them with 3D laser scan data. Finally, the application scope and limitations of the newly proposed approach were further addressed.http://www.sciencedirect.com/science/article/pii/S1674775524000969PhotogrammetryShooting parameterJRC estimation3D reconstruction |
| spellingShingle | Qinzheng Yang Ang Li Feng Dai Zhen Cui Hongtian Wang Improvement of photogrammetric joint roughness coefficient value by integrating automatic shooting parameter selection and composite error model Journal of Rock Mechanics and Geotechnical Engineering Photogrammetry Shooting parameter JRC estimation 3D reconstruction |
| title | Improvement of photogrammetric joint roughness coefficient value by integrating automatic shooting parameter selection and composite error model |
| title_full | Improvement of photogrammetric joint roughness coefficient value by integrating automatic shooting parameter selection and composite error model |
| title_fullStr | Improvement of photogrammetric joint roughness coefficient value by integrating automatic shooting parameter selection and composite error model |
| title_full_unstemmed | Improvement of photogrammetric joint roughness coefficient value by integrating automatic shooting parameter selection and composite error model |
| title_short | Improvement of photogrammetric joint roughness coefficient value by integrating automatic shooting parameter selection and composite error model |
| title_sort | improvement of photogrammetric joint roughness coefficient value by integrating automatic shooting parameter selection and composite error model |
| topic | Photogrammetry Shooting parameter JRC estimation 3D reconstruction |
| url | http://www.sciencedirect.com/science/article/pii/S1674775524000969 |
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