A Surrogate Model for the Rapid Prediction of Factor of Safety in Slopes with Spatial Variability
Due to the complexity and long-term nature of geological evolution, geotechnical strength parameters exhibit significant spatial variability, which has a crucial impact on slope stability assessment. While traditional numerical methods combined with Monte Carlo simulations and Gaussian random field...
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| Format: | Article |
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MDPI AG
2025-05-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/10/1604 |
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| author | Xitailang Cao Shan Lin Miao Dong Quanke Hu Hong Zheng |
| author_facet | Xitailang Cao Shan Lin Miao Dong Quanke Hu Hong Zheng |
| author_sort | Xitailang Cao |
| collection | DOAJ |
| description | Due to the complexity and long-term nature of geological evolution, geotechnical strength parameters exhibit significant spatial variability, which has a crucial impact on slope stability assessment. While traditional numerical methods combined with Monte Carlo simulations and Gaussian random field theory provide accurate stability analysis, their high computational cost makes them impractical for large-scale engineering applications. To address this issue, this study proposes an efficient surrogate modeling approach for the rapid prediction of the factor of safety in slopes while considering the spatial variability of geotechnical parameters. The accuracy and robustness of the proposed model are validated through a single-layer slope case study. Results demonstrate that this approach not only ensures computational accuracy but also significantly enhances efficiency. Compared with conventional methods, the surrogate model effectively replaces high-cost numerical simulations, offering a practical and efficient solution for slope stability analysis under complex geological conditions, with high potential for engineering applications. |
| format | Article |
| id | doaj-art-ee792dc2998940e18c0416814946bf74 |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-ee792dc2998940e18c0416814946bf742025-08-20T03:47:57ZengMDPI AGMathematics2227-73902025-05-011310160410.3390/math13101604A Surrogate Model for the Rapid Prediction of Factor of Safety in Slopes with Spatial VariabilityXitailang Cao0Shan Lin1Miao Dong2Quanke Hu3Hong Zheng4Key Laboratory of Urban Security and Disaster Engineering, Ministry of Education, Beijing University of Technology, Beijing 100124, ChinaKey Laboratory of Urban Security and Disaster Engineering, Ministry of Education, Beijing University of Technology, Beijing 100124, ChinaKey Laboratory of Urban Security and Disaster Engineering, Ministry of Education, Beijing University of Technology, Beijing 100124, ChinaKey Laboratory of Urban Security and Disaster Engineering, Ministry of Education, Beijing University of Technology, Beijing 100124, ChinaKey Laboratory of Urban Security and Disaster Engineering, Ministry of Education, Beijing University of Technology, Beijing 100124, ChinaDue to the complexity and long-term nature of geological evolution, geotechnical strength parameters exhibit significant spatial variability, which has a crucial impact on slope stability assessment. While traditional numerical methods combined with Monte Carlo simulations and Gaussian random field theory provide accurate stability analysis, their high computational cost makes them impractical for large-scale engineering applications. To address this issue, this study proposes an efficient surrogate modeling approach for the rapid prediction of the factor of safety in slopes while considering the spatial variability of geotechnical parameters. The accuracy and robustness of the proposed model are validated through a single-layer slope case study. Results demonstrate that this approach not only ensures computational accuracy but also significantly enhances efficiency. Compared with conventional methods, the surrogate model effectively replaces high-cost numerical simulations, offering a practical and efficient solution for slope stability analysis under complex geological conditions, with high potential for engineering applications.https://www.mdpi.com/2227-7390/13/10/1604surrogate modelconvolutional neural networkfactor of safetyspatial variabilityslope stability |
| spellingShingle | Xitailang Cao Shan Lin Miao Dong Quanke Hu Hong Zheng A Surrogate Model for the Rapid Prediction of Factor of Safety in Slopes with Spatial Variability Mathematics surrogate model convolutional neural network factor of safety spatial variability slope stability |
| title | A Surrogate Model for the Rapid Prediction of Factor of Safety in Slopes with Spatial Variability |
| title_full | A Surrogate Model for the Rapid Prediction of Factor of Safety in Slopes with Spatial Variability |
| title_fullStr | A Surrogate Model for the Rapid Prediction of Factor of Safety in Slopes with Spatial Variability |
| title_full_unstemmed | A Surrogate Model for the Rapid Prediction of Factor of Safety in Slopes with Spatial Variability |
| title_short | A Surrogate Model for the Rapid Prediction of Factor of Safety in Slopes with Spatial Variability |
| title_sort | surrogate model for the rapid prediction of factor of safety in slopes with spatial variability |
| topic | surrogate model convolutional neural network factor of safety spatial variability slope stability |
| url | https://www.mdpi.com/2227-7390/13/10/1604 |
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