Determination of Joint Surface Roughness Based on 3D Statistical Morphology Characteristic
Roughness significantly affects the shear behavior of rock joints, which are widely encountered in geotechnical engineering. Since the existing calculation methods on the joint roughness coefficient (JRC) fail to obtain a sufficiently accurate value of JRC, a new determination method was proposed in...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/8813409 |
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| author | Hang Lin Jianxin Qin Yixian Wang Yifan Chen |
| author_facet | Hang Lin Jianxin Qin Yixian Wang Yifan Chen |
| author_sort | Hang Lin |
| collection | DOAJ |
| description | Roughness significantly affects the shear behavior of rock joints, which are widely encountered in geotechnical engineering. Since the existing calculation methods on the joint roughness coefficient (JRC) fail to obtain a sufficiently accurate value of JRC, a new determination method was proposed in this study, where the 3D laser scanning technique and self-compiled Python code, as well as the statistical parameter methods, were applied. Then, the shear strength of jointed rock was evaluated via Barton's model, and therefore, a comprehensive comparison between the calculating results and experimental results was executed. Ultimately, the influencing factors of roughness profile extraction on the accuracy of JRC value, such as the measuring point interval, profile number, and measuring direction, were investigated. The results show that (1) equipped with the 3D laser scanning technique, the roughness profiles can be accurately extracted via the self-compiled Python code, (2) an excellent consistency of shear strength could be observed between the calculating value and experimental results, verifying the validity and accuracy of the proposed method, and (3) a smaller measuring point interval can produce a more accurate digital profile and more accurate JRC value. To a certain extent, the more the sample numbers of profiles, the smaller the value of JRC. |
| format | Article |
| id | doaj-art-55265b4c31f74adcb84ea0f8b83749e8 |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-55265b4c31f74adcb84ea0f8b83749e82025-02-03T06:12:45ZengWileyAdvances in Civil Engineering1687-80861687-80942021-01-01202110.1155/2021/88134098813409Determination of Joint Surface Roughness Based on 3D Statistical Morphology CharacteristicHang Lin0Jianxin Qin1Yixian Wang2Yifan Chen3School of Resources and Safety Engineering, Central South University, Changsha, Hunan 410083, ChinaSchool of Resources and Safety Engineering, Central South University, Changsha, Hunan 410083, ChinaSchool of Civil and Hydraulic Engineering, Hefei University of Technology, Hefei, Anhui 230009, ChinaSchool of Resources and Safety Engineering, Central South University, Changsha, Hunan 410083, ChinaRoughness significantly affects the shear behavior of rock joints, which are widely encountered in geotechnical engineering. Since the existing calculation methods on the joint roughness coefficient (JRC) fail to obtain a sufficiently accurate value of JRC, a new determination method was proposed in this study, where the 3D laser scanning technique and self-compiled Python code, as well as the statistical parameter methods, were applied. Then, the shear strength of jointed rock was evaluated via Barton's model, and therefore, a comprehensive comparison between the calculating results and experimental results was executed. Ultimately, the influencing factors of roughness profile extraction on the accuracy of JRC value, such as the measuring point interval, profile number, and measuring direction, were investigated. The results show that (1) equipped with the 3D laser scanning technique, the roughness profiles can be accurately extracted via the self-compiled Python code, (2) an excellent consistency of shear strength could be observed between the calculating value and experimental results, verifying the validity and accuracy of the proposed method, and (3) a smaller measuring point interval can produce a more accurate digital profile and more accurate JRC value. To a certain extent, the more the sample numbers of profiles, the smaller the value of JRC.http://dx.doi.org/10.1155/2021/8813409 |
| spellingShingle | Hang Lin Jianxin Qin Yixian Wang Yifan Chen Determination of Joint Surface Roughness Based on 3D Statistical Morphology Characteristic Advances in Civil Engineering |
| title | Determination of Joint Surface Roughness Based on 3D Statistical Morphology Characteristic |
| title_full | Determination of Joint Surface Roughness Based on 3D Statistical Morphology Characteristic |
| title_fullStr | Determination of Joint Surface Roughness Based on 3D Statistical Morphology Characteristic |
| title_full_unstemmed | Determination of Joint Surface Roughness Based on 3D Statistical Morphology Characteristic |
| title_short | Determination of Joint Surface Roughness Based on 3D Statistical Morphology Characteristic |
| title_sort | determination of joint surface roughness based on 3d statistical morphology characteristic |
| url | http://dx.doi.org/10.1155/2021/8813409 |
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