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

<p>In this paper, based on sampling and analysis of a large number of soft and weak sandwich slope data, several factors that have a great influence on slope stability are established, and a predictive analysis model describing the stability of soft and weak sandwich slopes is established by using the now more advanced partial least squares method. Then, for the traditional partial least squares method that is not suitable for the non-linear stability coefficient of the weak sandwich to slope stability prediction, the recursive partial least squares method with forgetting factor is proposed for the weak sandwich to slope stability prediction analysis to solve the problem of stability prediction lag. Finally, based on elastic mechanics, elastic fracture mechanics and unsaturated soil mechanics, the structure of soft and weak interlayers on slopes and their stability strength are studied, and the performance of MATLAB-based partial least squares method for slope stability prediction analysis is verified by designing orthogonal experiments. The results show that the predicted values do not differ much from the results of finite element calculation, the absolute errors are all less than 0.15, and there are 5 absolute non-differences less than 0.1, accounting for 62.5% of the total number of predicted groups. The relative errors were less than 6%. This study shows that the partial least squares method can deal with the nonlinear mapping relationship between slope stability and influencing factors well and can make more accurate and objective prediction results on the stability of slopes.</p>
</abstract>

In this paper, based on sampling and analysis of a large number of soft and weak sandwich slope data, several factors that have a great influence on slope stability are established, and a predictive analysis model describing the stability of soft and weak sandwich slopes is established by using the now more advanced partial least squares method. Then, for the traditional partial least squares method that is not suitable for the non-linear stability coefficient of the weak sandwich to slope stability prediction, the recursive partial least squares method with forgetting factor is proposed for the weak sandwich to slope stability prediction analysis to solve the problem of stability prediction lag. Finally, based on elastic mechanics, elastic fracture mechanics and unsaturated soil mechanics, the structure of soft and weak interlayers on slopes and their stability strength are studied, and the performance of MATLAB-based partial least squares method for slope stability prediction analysis is verified by designing orthogonal experiments. The results show that the predicted values do not differ much from the results of finite element calculation, the absolute errors are all less than 0.15, and there are 5 absolute non-differences less than 0.1, accounting for 62.5% of the total number of predicted groups. The relative errors were less than 6%. This study shows that the partial least squares method can deal with the nonlinear mapping relationship between slope stability and influencing factors well and can make more accurate and objective prediction results on the stability of slopes.

Prediction analysis of slope stability due to soft and weak interlayers based on partial least squares method

Applied Mathematics and Nonlinear Sciences

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

In this paper, based on sampling and analysis of a large number of soft and weak sandwich slope data, several factors that have a great influence on slope...

Prediction analysis of slope stability due to soft and weak interlayers based on partial least squares method

Online veröffentlicht: 23. Sept. 2023

Seitenbereich: -

Eingereicht: 27. Nov. 2022

Akzeptiert: 03. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00364

Schlüsselwörter
Partial least squares, Forgetting factor, Soft and weak interlayers on slopes, Orthogonal experiments, Stability

© 2023 Kai Cui, published by Sciendo

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

Prediction analysis of slope stability due to soft and weak interlayers based on partial least squares method

Online veröffentlicht: 23. Sept. 2023

Seitenbereich: -

Eingereicht: 27. Nov. 2022

Akzeptiert: 03. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00364

SchlüsselwörterPartial least squares, Forgetting factor, Soft and weak interlayers on slopes, Orthogonal experiments, Stability

© 2023 Kai Cui, published by Sciendo

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

Schlüsselwörter
Partial least squares, Forgetting factor, Soft and weak interlayers on slopes, Orthogonal experiments, Stability