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Seismic Stability Assessment of Rock Slopes Using Multivariate Adaptive Regression Splines
https://orcid.org/http://orcid.org/0000-0002-6814-5797
This paper delves into an in-depth exploration of stability solutions for rock slopes, focusing on the Hoek–Brown failure criterion for rock. The study takes into account the influence of pseudo-static seismic body forces on rock slope behavior. To achieve stability analysis, the finite element limit analysis (FELA) technique is adopted, enabling the derivation of reliable stability results. In addition to FELA, this research introduces a novel approach for future applications—a machine learning model based on the multivariate adaptive regression splines (MARS) method. The proposed MARS model equation is meticulously verified and validated, yielding a remarkable agreement with numerical results, as indicated by an impressive R2 value of 99.82%. Moreover, this study meticulously investigates the impact of various key variables, including the strength parameters of rock masses and slope geometry. Through rigorous sensitivity analysis, the relative importance of these selected variables is unveiled, providing invaluable insights for practitioners in the field of engineering. The proposed MARS expression, along with the sensitivity analysis results, not only serves as a theoretical guideline but also offers an efficient tool for engineering practitioners engaged in rock slope stability assessments.
Seismic Stability Assessment of Rock Slopes Using Multivariate Adaptive Regression Splines
https://orcid.org/http://orcid.org/0000-0002-6814-5797
This paper delves into an in-depth exploration of stability solutions for rock slopes, focusing on the Hoek–Brown failure criterion for rock. The study takes into account the influence of pseudo-static seismic body forces on rock slope behavior. To achieve stability analysis, the finite element limit analysis (FELA) technique is adopted, enabling the derivation of reliable stability results. In addition to FELA, this research introduces a novel approach for future applications—a machine learning model based on the multivariate adaptive regression splines (MARS) method. The proposed MARS model equation is meticulously verified and validated, yielding a remarkable agreement with numerical results, as indicated by an impressive R2 value of 99.82%. Moreover, this study meticulously investigates the impact of various key variables, including the strength parameters of rock masses and slope geometry. Through rigorous sensitivity analysis, the relative importance of these selected variables is unveiled, providing invaluable insights for practitioners in the field of engineering. The proposed MARS expression, along with the sensitivity analysis results, not only serves as a theoretical guideline but also offers an efficient tool for engineering practitioners engaged in rock slope stability assessments.
Seismic Stability Assessment of Rock Slopes Using Multivariate Adaptive Regression Splines
https://orcid.org/http://orcid.org/0000-0002-6814-5797
This paper delves into an in-depth exploration of stability solutions for rock slopes, focusing on the Hoek–Brown failure criterion for rock. The study takes into account the influence of pseudo-static seismic body forces on rock slope behavior. To achieve stability analysis, the finite element limit analysis (FELA) technique is adopted, enabling the derivation of reliable stability results. In addition to FELA, this research introduces a novel approach for future applications—a machine learning model based on the multivariate adaptive regression splines (MARS) method. The proposed MARS model equation is meticulously verified and validated, yielding a remarkable agreement with numerical results, as indicated by an impressive R2 value of 99.82%. Moreover, this study meticulously investigates the impact of various key variables, including the strength parameters of rock masses and slope geometry. Through rigorous sensitivity analysis, the relative importance of these selected variables is unveiled, providing invaluable insights for practitioners in the field of engineering. The proposed MARS expression, along with the sensitivity analysis results, not only serves as a theoretical guideline but also offers an efficient tool for engineering practitioners engaged in rock slope stability assessments.
Seismic Stability Assessment of Rock Slopes Using Multivariate Adaptive Regression Splines
Transp. Infrastruct. Geotech.
Keawsawasvong, Suraparb (author) / Kounlavong, Khamnoy (author) / Duong, Nhat Tan (author) / Lai, Van Qui (author) / Khatri, Vishwas Nandkishor (author) / Eskandarinejad, Alireza (author)
Transportation Infrastructure Geotechnology ; 11 ; 2296-2318
2024-08-01
23 pages
Article (Journal)
Electronic Resource
English
Seismic Stability Assessment of Rock Slopes Using Multivariate Adaptive Regression Splines
| Springer Verlag | 2024
| Springer Verlag | 2022
| British Library Online Contents | 2017
| British Library Online Contents | 2017