A method to predict the peak shear strength of rock joints based on machine learning
In geotechnical and tunneling engineering, accurately determining the mechanical properties of jointed rock holds great significance for project safety assessments. Peak shear strength (PSS), being the paramount mechanical property of joints, has been a focal point in the research field. There are l...
| Main Authors: | , , , , , |
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| Format: | Journal Article |
| Published: |
2023
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| Online Access: | http://hdl.handle.net/20.500.11937/94647 |
| _version_ | 1848765894092652544 |
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| author | Ban, L.R. Zhu, C. Hou, Y.H. Du, W.S. Qi, C.Z. Lu, Chunsheng |
| author_facet | Ban, L.R. Zhu, C. Hou, Y.H. Du, W.S. Qi, C.Z. Lu, Chunsheng |
| author_sort | Ban, L.R. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In geotechnical and tunneling engineering, accurately determining the mechanical properties of jointed rock holds great significance for project safety assessments. Peak shear strength (PSS), being the paramount mechanical property of joints, has been a focal point in the research field. There are limitations in the current peak shear strength (PSS) prediction models for jointed rock: (i) the models do not comprehensively consider various influencing factors, and a PSS prediction model covering seven factors has not been established, including the sampling interval of the joints, the surface roughness of the joints, the normal stress, the basic friction angle, the uniaxial tensile strength, the uniaxial compressive strength, and the joint size for coupled joints; (ii) the datasets used to train the models are relatively limited; and (iii) there is a controversy regarding whether compressive or tensile strength should be used as the strength term among the influencing factors. To overcome these limitations, we developed four machine learning models covering these seven influencing factors, three relying on Support Vector Regression (SVR) with different kernel functions (linear, polynomial, and Radial Basis Function (RBF)) and one using deep learning (DL). Based on these seven influencing factors, we compiled a dataset comprising the outcomes of 493 published direct shear tests for the training and validation of these four models. We compared the prediction performance of these four machine learning models with Tang’s and Tatone’s models. The prediction errors of Tang’s and Tatone’s models are 21.8% and 17.7%, respectively, while SVR_linear is at 16.6%, SVR_poly is at 14.0%, and SVR_RBF is at 12.1%. DL outperforms the two existing models with only an 8.5% error. Additionally, we performed shear tests on granite joints to validate the predictive capability of the DL-based model. With the DL approach, the results suggest that uniaxial tensile strength is recommended as the material strength term in the PSS model for more reliable outcomes. |
| first_indexed | 2025-11-14T11:42:30Z |
| format | Journal Article |
| id | curtin-20.500.11937-94647 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:42:30Z |
| publishDate | 2023 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-946472024-05-03T02:58:24Z A method to predict the peak shear strength of rock joints based on machine learning Ban, L.R. Zhu, C. Hou, Y.H. Du, W.S. Qi, C.Z. Lu, Chunsheng In geotechnical and tunneling engineering, accurately determining the mechanical properties of jointed rock holds great significance for project safety assessments. Peak shear strength (PSS), being the paramount mechanical property of joints, has been a focal point in the research field. There are limitations in the current peak shear strength (PSS) prediction models for jointed rock: (i) the models do not comprehensively consider various influencing factors, and a PSS prediction model covering seven factors has not been established, including the sampling interval of the joints, the surface roughness of the joints, the normal stress, the basic friction angle, the uniaxial tensile strength, the uniaxial compressive strength, and the joint size for coupled joints; (ii) the datasets used to train the models are relatively limited; and (iii) there is a controversy regarding whether compressive or tensile strength should be used as the strength term among the influencing factors. To overcome these limitations, we developed four machine learning models covering these seven influencing factors, three relying on Support Vector Regression (SVR) with different kernel functions (linear, polynomial, and Radial Basis Function (RBF)) and one using deep learning (DL). Based on these seven influencing factors, we compiled a dataset comprising the outcomes of 493 published direct shear tests for the training and validation of these four models. We compared the prediction performance of these four machine learning models with Tang’s and Tatone’s models. The prediction errors of Tang’s and Tatone’s models are 21.8% and 17.7%, respectively, while SVR_linear is at 16.6%, SVR_poly is at 14.0%, and SVR_RBF is at 12.1%. DL outperforms the two existing models with only an 8.5% error. Additionally, we performed shear tests on granite joints to validate the predictive capability of the DL-based model. With the DL approach, the results suggest that uniaxial tensile strength is recommended as the material strength term in the PSS model for more reliable outcomes. 2023 Journal Article http://hdl.handle.net/20.500.11937/94647 10.1007/s11629-023-8048-z restricted |
| spellingShingle | Ban, L.R. Zhu, C. Hou, Y.H. Du, W.S. Qi, C.Z. Lu, Chunsheng A method to predict the peak shear strength of rock joints based on machine learning |
| title | A method to predict the peak shear strength of rock joints based on machine learning |
| title_full | A method to predict the peak shear strength of rock joints based on machine learning |
| title_fullStr | A method to predict the peak shear strength of rock joints based on machine learning |
| title_full_unstemmed | A method to predict the peak shear strength of rock joints based on machine learning |
| title_short | A method to predict the peak shear strength of rock joints based on machine learning |
| title_sort | method to predict the peak shear strength of rock joints based on machine learning |
| url | http://hdl.handle.net/20.500.11937/94647 |