A machine learning-based method for predicting the shear behaviors of rock joints
In this study, machine learning prediction models (MLPMs), including artificial neural network (ANN), support vector regression (SVR), K-nearest neighbors (KNN), and random forest (RF) algorithms, were developed to predict the peak shear stress values and shear stress-displacement curves of rock joi...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Soils and Foundations |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0038080624000957 |
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| author | Liu He Yu Tan Timothy Copeland Jiannan Chen Qiang Tang |
| author_facet | Liu He Yu Tan Timothy Copeland Jiannan Chen Qiang Tang |
| author_sort | Liu He |
| collection | DOAJ |
| description | In this study, machine learning prediction models (MLPMs), including artificial neural network (ANN), support vector regression (SVR), K-nearest neighbors (KNN), and random forest (RF) algorithms, were developed to predict the peak shear stress values and shear stress-displacement curves of rock joints. The database used contained 693 records of peak shear stress and 162 original shear stress-displacement curves derived from direct shear tests. The results demonstrated that the MLPMs provided reliable predictions for shear stress, with the mean squared errors (MSEs) between their predicted and measured shear stress varying from 0.003 to 0.069 and the coefficients of determination (R2 values) varying from 0.964 to 0.998. The feature importance values indicate that the joint surface roughness coefficient (JRC) is the most important influential factor in determining the peak shear stress, followed by the joint wall compressive strength (JCS), basic friction angle (φb), and shear surface area (As). Similarly, for the shear stress-displacement curve, the JRC is the dominant factor, followed by As, φb, and JCS. Additional direct shear tests were conducted for model validation. The validation shows that the MLPM predictions demonstrate improved consistency with the experimental results in relation to both the peak shear stress and peak shear displacement. |
| format | Article |
| id | doaj-art-61c5226ed3bb4f77b6eeba009f969f1e |
| institution | OA Journals |
| issn | 2524-1788 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Soils and Foundations |
| spelling | doaj-art-61c5226ed3bb4f77b6eeba009f969f1e2025-08-20T02:35:47ZengElsevierSoils and Foundations2524-17882024-12-0164610151710.1016/j.sandf.2024.101517A machine learning-based method for predicting the shear behaviors of rock jointsLiu He0Yu Tan1Timothy Copeland2Jiannan Chen3Qiang Tang4Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu, Sichuan, PR ChinaDepartment of Civil and Environmental Engineering, University of Wisconsin, Madison, WI, USAGeosyntec Consultants Inc., Orlando, FL, USA; Formerly Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL, USADepartment of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL, USASchool of Rail Transportation, Soochow University, Yangchenghu Campus, Xiangcheng District, Suzhou 215131, PR China; Corresponding author.In this study, machine learning prediction models (MLPMs), including artificial neural network (ANN), support vector regression (SVR), K-nearest neighbors (KNN), and random forest (RF) algorithms, were developed to predict the peak shear stress values and shear stress-displacement curves of rock joints. The database used contained 693 records of peak shear stress and 162 original shear stress-displacement curves derived from direct shear tests. The results demonstrated that the MLPMs provided reliable predictions for shear stress, with the mean squared errors (MSEs) between their predicted and measured shear stress varying from 0.003 to 0.069 and the coefficients of determination (R2 values) varying from 0.964 to 0.998. The feature importance values indicate that the joint surface roughness coefficient (JRC) is the most important influential factor in determining the peak shear stress, followed by the joint wall compressive strength (JCS), basic friction angle (φb), and shear surface area (As). Similarly, for the shear stress-displacement curve, the JRC is the dominant factor, followed by As, φb, and JCS. Additional direct shear tests were conducted for model validation. The validation shows that the MLPM predictions demonstrate improved consistency with the experimental results in relation to both the peak shear stress and peak shear displacement.http://www.sciencedirect.com/science/article/pii/S0038080624000957Rock jointMachine learning prediction modelsShear behavior predictionFeature importanceDirect shear test |
| spellingShingle | Liu He Yu Tan Timothy Copeland Jiannan Chen Qiang Tang A machine learning-based method for predicting the shear behaviors of rock joints Soils and Foundations Rock joint Machine learning prediction models Shear behavior prediction Feature importance Direct shear test |
| title | A machine learning-based method for predicting the shear behaviors of rock joints |
| title_full | A machine learning-based method for predicting the shear behaviors of rock joints |
| title_fullStr | A machine learning-based method for predicting the shear behaviors of rock joints |
| title_full_unstemmed | A machine learning-based method for predicting the shear behaviors of rock joints |
| title_short | A machine learning-based method for predicting the shear behaviors of rock joints |
| title_sort | machine learning based method for predicting the shear behaviors of rock joints |
| topic | Rock joint Machine learning prediction models Shear behavior prediction Feature importance Direct shear test |
| url | http://www.sciencedirect.com/science/article/pii/S0038080624000957 |
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