Hybrid Machine Learning Model for Predicting Shear Strength of Rock Joints
The accurate prediction of joint shear strength is crucial for rock mass engineering design and geological hazard assessment. However, traditional machine learning (ML) models often suffer from local optima and limited generalization ability when dealing with complex nonlinear problems, thereby comp...
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MDPI AG
2025-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/13/7097 |
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| author | Daxing Lei Yaoping Zhang Zhigang Lu Hang Lin Yifan Chen |
| author_facet | Daxing Lei Yaoping Zhang Zhigang Lu Hang Lin Yifan Chen |
| author_sort | Daxing Lei |
| collection | DOAJ |
| description | The accurate prediction of joint shear strength is crucial for rock mass engineering design and geological hazard assessment. However, traditional machine learning (ML) models often suffer from local optima and limited generalization ability when dealing with complex nonlinear problems, thereby compromising prediction accuracy and stability. To address these challenges, this study proposes a hybrid ML model that integrates a multilayer perceptron (MLP) with the slime mold algorithm (SMA), termed the SMA-MLP model. While MLP exhibits strong nonlinear mapping capability, SMA enhances its training process through global optimization and parameter tuning, thereby improving predictive accuracy and robustness. A dataset with five input variables was constructed to evaluate the performance of the SMA-MLP model comprehensively. The proposed model was compared with other ML models. The results indicate that SMA-MLP outperforms these models in key metrics such as the root mean squared error (RMSE) and the correlation coefficient (R<sup>2</sup>), achieving an R<sup>2</sup> of 0.97 and an RMSE as low as 0.10 MPa on the test set. Furthermore, feature importance analysis reveals that normal stress has the most significant influence on joint shear strength. This study demonstrates the superiority of SMA-MLP in predicting joint shear strength, highlighting its potential as an efficient and accurate tool for rock mass engineering analysis and providing reliable technical support for geological hazard assessment. |
| format | Article |
| id | doaj-art-384c805fdf9446da9646a0beafde6a2c |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-384c805fdf9446da9646a0beafde6a2c2025-08-20T02:35:46ZengMDPI AGApplied Sciences2076-34172025-06-011513709710.3390/app15137097Hybrid Machine Learning Model for Predicting Shear Strength of Rock JointsDaxing Lei0Yaoping Zhang1Zhigang Lu2Hang Lin3Yifan Chen4School of Resources and Civil Engineering, GanNan University of Science and Technology, Ganzhou 341000, ChinaSchool of Resources and Civil Engineering, GanNan University of Science and Technology, Ganzhou 341000, ChinaSchool of Resources and Civil Engineering, GanNan University of Science and Technology, Ganzhou 341000, ChinaSchool of Resources and Safety Engineering, Central South University, Changsha 410083, ChinaSchool of Resources and Safety Engineering, Central South University, Changsha 410083, ChinaThe accurate prediction of joint shear strength is crucial for rock mass engineering design and geological hazard assessment. However, traditional machine learning (ML) models often suffer from local optima and limited generalization ability when dealing with complex nonlinear problems, thereby compromising prediction accuracy and stability. To address these challenges, this study proposes a hybrid ML model that integrates a multilayer perceptron (MLP) with the slime mold algorithm (SMA), termed the SMA-MLP model. While MLP exhibits strong nonlinear mapping capability, SMA enhances its training process through global optimization and parameter tuning, thereby improving predictive accuracy and robustness. A dataset with five input variables was constructed to evaluate the performance of the SMA-MLP model comprehensively. The proposed model was compared with other ML models. The results indicate that SMA-MLP outperforms these models in key metrics such as the root mean squared error (RMSE) and the correlation coefficient (R<sup>2</sup>), achieving an R<sup>2</sup> of 0.97 and an RMSE as low as 0.10 MPa on the test set. Furthermore, feature importance analysis reveals that normal stress has the most significant influence on joint shear strength. This study demonstrates the superiority of SMA-MLP in predicting joint shear strength, highlighting its potential as an efficient and accurate tool for rock mass engineering analysis and providing reliable technical support for geological hazard assessment.https://www.mdpi.com/2076-3417/15/13/7097rock jointsshear strengthmachine learningslime mold algorithm |
| spellingShingle | Daxing Lei Yaoping Zhang Zhigang Lu Hang Lin Yifan Chen Hybrid Machine Learning Model for Predicting Shear Strength of Rock Joints Applied Sciences rock joints shear strength machine learning slime mold algorithm |
| title | Hybrid Machine Learning Model for Predicting Shear Strength of Rock Joints |
| title_full | Hybrid Machine Learning Model for Predicting Shear Strength of Rock Joints |
| title_fullStr | Hybrid Machine Learning Model for Predicting Shear Strength of Rock Joints |
| title_full_unstemmed | Hybrid Machine Learning Model for Predicting Shear Strength of Rock Joints |
| title_short | Hybrid Machine Learning Model for Predicting Shear Strength of Rock Joints |
| title_sort | hybrid machine learning model for predicting shear strength of rock joints |
| topic | rock joints shear strength machine learning slime mold algorithm |
| url | https://www.mdpi.com/2076-3417/15/13/7097 |
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