<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Slope stability is a critical area of research pivotal to ensuring public safety and advancing societal development. A thorough examination of slope stability holds substantial practical value in augmenting geological disaster prevention and shaping engineering economic strategies. This study develops an optimized Response Surface Methodology (RSM) model by incorporating the main effect analysis to enhance the foundational response surface method. Subsequently, to address the requirements of slope stability analysis, a numerical model calculating the stress and reliability of anti-slip piles is formulated. Building on this foundation, the Kriging model is refined using a particle swarm optimization algorithm, which is then integrated with the RSM optimization model to evaluate slope stability in geotechnical engineering applications. The efficacy of the proposed method is underscored by its performance across five-speed boundaries, where it achieves a mean reliability value of 90.40%, with a minimal average error of 0.69%. Moreover, empirical validation of the method through conditional probability calculations yields a value of 19.95%. This figure closely aligns with the results derived from the narrow-boundary method, which estimates slope system failure probabilities between 19.28% and 22.54%. Comparatively, the system failure probability determined by the Probabilistic Network Evaluation Technique (PNET) stands at 20.75%. These results affirm that the RSM-based optimization model described in this study is not only precise but also productive, establishing it as a robust method for conducting slope stability analysis in geotechnical engineering.</p>
</abstract>

Slope stability is a critical area of research pivotal to ensuring public safety and advancing societal development. A thorough examination of slope stability holds substantial practical value in augmenting geological disaster prevention and shaping engineering economic strategies. This study develops an optimized Response Surface Methodology (RSM) model by incorporating the main effect analysis to enhance the foundational response surface method. Subsequently, to address the requirements of slope stability analysis, a numerical model calculating the stress and reliability of anti-slip piles is formulated. Building on this foundation, the Kriging model is refined using a particle swarm optimization algorithm, which is then integrated with the RSM optimization model to evaluate slope stability in geotechnical engineering applications. The efficacy of the proposed method is underscored by its performance across five-speed boundaries, where it achieves a mean reliability value of 90.40%, with a minimal average error of 0.69%. Moreover, empirical validation of the method through conditional probability calculations yields a value of 19.95%. This figure closely aligns with the results derived from the narrow-boundary method, which estimates slope system failure probabilities between 19.28% and 22.54%. Comparatively, the system failure probability determined by the Probabilistic Network Evaluation Technique (PNET) stands at 20.75%. These results affirm that the RSM-based optimization model described in this study is not only precise but also productive, establishing it as a robust method for conducting slope stability analysis in geotechnical engineering.

Application of RSM optimization model for slope stability analysis in geotechnical engineering

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Slope stability is a critical area of research pivotal to ensuring public safety and advancing societal development. A thorough examination of slope stability...

Application of RSM optimization model for slope stability analysis in geotechnical engineering

Publicado en línea: 05 jul 2024

Páginas: -

Recibido: 01 mar 2024

Aceptado: 16 may 2024

DOI: https://doi.org/10.2478/amns-2024-1722

Palabras clave
RSM, Particle swarm optimization, Kriging model, Slope stability, Geotechnical engineering

© 2024 Kai Shang., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Application of RSM optimization model for slope stability analysis in geotechnical engineering

Publicado en línea: 05 jul 2024

Páginas: -

Recibido: 01 mar 2024

Aceptado: 16 may 2024

DOI: https://doi.org/10.2478/amns-2024-1722

Palabras claveRSM, Particle swarm optimization, Kriging model, Slope stability, Geotechnical engineering

© 2024 Kai Shang., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Palabras clave
RSM, Particle swarm optimization, Kriging model, Slope stability, Geotechnical engineering