A novel inversion method of slope rock mechanical parameters using differential evolution gray wolf algorithm to optimize support vector regression
IntroductionThe stability evaluation and deformation prediction in geotechnical engineering depend on accurate rock mass mechanical parameters (RMMPs). The selection of these parameters directly influences the reliability of analysis. The conventional techniques used to assess the RMMPs face conside...
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Earth Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2025.1575194/full |
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| author | Tingkai Hou Zonghong Zhou Yonggang Zhang Jing Zhang |
| author_facet | Tingkai Hou Zonghong Zhou Yonggang Zhang Jing Zhang |
| author_sort | Tingkai Hou |
| collection | DOAJ |
| description | IntroductionThe stability evaluation and deformation prediction in geotechnical engineering depend on accurate rock mass mechanical parameters (RMMPs). The selection of these parameters directly influences the reliability of analysis. The conventional techniques used to assess the RMMPs face considerable challenges in real-world applications, which necessitates the need to investigate novel approaches.MethodsThis paper proposes a displacement back-analysis (DBA) approach that utilizes support vector regression (SVR) optimized by differential evolution grey wolf algorithm (DE-GWO) to invert the RMMPs, which improves global optimization capability and inversion accuracy. Firstly, the uniform test design method is employed to outline the RMMPs for inversion, anddisplacement calculations are performed using FLAC3D to generate learning and testing samples. Secondly, the DE-GWO, particle swarm optimization (PSO), genetic algorithm (GA), and SVR are integrated to identify the optimal superparameters, while the nonlinear mapping relationship between inversion parameters and displacements is established. Finally, the mechanical parameters to be measured are inversed based on field-measured displacements. This model is utilized to invert the RMMPs for a mining site located in Yunnan Province, and the inversed RMMPs are utilized for forward analysis. The results demonstrate that the DE-GWO-SVR method achieves the best results but requires the shortest inversion time.ResultsThe inversed RMMPs fall within acceptable ranges, while the error between the forward and monitored displacements is less than 10%, with a maximum deviation of 9.52%. This research introduces an innovative approach for assessing the RMMPs. |
| format | Article |
| id | doaj-art-bfdbcaee6d3a4c309d680e1194b456e2 |
| institution | OA Journals |
| issn | 2296-6463 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Earth Science |
| spelling | doaj-art-bfdbcaee6d3a4c309d680e1194b456e22025-08-20T02:26:20ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632025-04-011310.3389/feart.2025.15751941575194A novel inversion method of slope rock mechanical parameters using differential evolution gray wolf algorithm to optimize support vector regressionTingkai Hou0Zonghong Zhou1Yonggang Zhang2Jing Zhang3Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming, Yunnan, ChinaFaculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming, Yunnan, ChinaChina Construction Eighth Bureau Engineering Research Institute, China Construction Eighth Engineering Division, Pudong, Shanghai, ChinaFaculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming, Yunnan, ChinaIntroductionThe stability evaluation and deformation prediction in geotechnical engineering depend on accurate rock mass mechanical parameters (RMMPs). The selection of these parameters directly influences the reliability of analysis. The conventional techniques used to assess the RMMPs face considerable challenges in real-world applications, which necessitates the need to investigate novel approaches.MethodsThis paper proposes a displacement back-analysis (DBA) approach that utilizes support vector regression (SVR) optimized by differential evolution grey wolf algorithm (DE-GWO) to invert the RMMPs, which improves global optimization capability and inversion accuracy. Firstly, the uniform test design method is employed to outline the RMMPs for inversion, anddisplacement calculations are performed using FLAC3D to generate learning and testing samples. Secondly, the DE-GWO, particle swarm optimization (PSO), genetic algorithm (GA), and SVR are integrated to identify the optimal superparameters, while the nonlinear mapping relationship between inversion parameters and displacements is established. Finally, the mechanical parameters to be measured are inversed based on field-measured displacements. This model is utilized to invert the RMMPs for a mining site located in Yunnan Province, and the inversed RMMPs are utilized for forward analysis. The results demonstrate that the DE-GWO-SVR method achieves the best results but requires the shortest inversion time.ResultsThe inversed RMMPs fall within acceptable ranges, while the error between the forward and monitored displacements is less than 10%, with a maximum deviation of 9.52%. This research introduces an innovative approach for assessing the RMMPs.https://www.frontiersin.org/articles/10.3389/feart.2025.1575194/fullrock mass mechanical parametersdisplacement inversionuniform testdifferential evolution grey wolf algorithmsupport vector regression (SVR) |
| spellingShingle | Tingkai Hou Zonghong Zhou Yonggang Zhang Jing Zhang A novel inversion method of slope rock mechanical parameters using differential evolution gray wolf algorithm to optimize support vector regression Frontiers in Earth Science rock mass mechanical parameters displacement inversion uniform test differential evolution grey wolf algorithm support vector regression (SVR) |
| title | A novel inversion method of slope rock mechanical parameters using differential evolution gray wolf algorithm to optimize support vector regression |
| title_full | A novel inversion method of slope rock mechanical parameters using differential evolution gray wolf algorithm to optimize support vector regression |
| title_fullStr | A novel inversion method of slope rock mechanical parameters using differential evolution gray wolf algorithm to optimize support vector regression |
| title_full_unstemmed | A novel inversion method of slope rock mechanical parameters using differential evolution gray wolf algorithm to optimize support vector regression |
| title_short | A novel inversion method of slope rock mechanical parameters using differential evolution gray wolf algorithm to optimize support vector regression |
| title_sort | novel inversion method of slope rock mechanical parameters using differential evolution gray wolf algorithm to optimize support vector regression |
| topic | rock mass mechanical parameters displacement inversion uniform test differential evolution grey wolf algorithm support vector regression (SVR) |
| url | https://www.frontiersin.org/articles/10.3389/feart.2025.1575194/full |
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