Slope Stability Prediction Based on GA-HIDMS-PSO-SVM

DOI

10.2991/978-94-6463-688-8_49How to use a DOI?

Keywords

Slope stability; Machine learning; GA-HIDMS-PSO-SVM model

Abstract

Slope instability leads to significant global economic losses annually. To evaluate slope stability swiftly and precisely, ensuring the safety of slope engineering. The GA-HIDMS-PSO-SVM algorithm is introduced to develop a slope stability prediction model. Six typical slope parameters, including bulk density, internal friction angle, cohesion, slope angle, slope height, and pore water pressure ratio, were selected as input factors, while the slope state was chosen as the output factor. The training dataset was built using data from 80 real-world engineering projects. The model achieves an accuracy value of 0.958, a recall rate of 0.959, a precision of 0.960, and an F1-score of 0.959, demonstrating exceptional prediction accuracy, robust generalization, and reliable results in identifying slope instability. Combined with engineering examples, it is proved that the evaluation results of slope state are consistent with the actual situation. The results show that GA-HIDMS-PSO-SVM model can be applied to slope stability prediction, which can provide basis for slope design and construction, and has a good application prospect in practical engineering application.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jinbo Zhang
AU  - Yubin Lin
AU  - Xinhong Li
AU  - Yuanhao Chen
AU  - Xue Chen
AU  - Dacai Chen
AU  - Xiaoxiang Chen
PY  - 2025
DA  - 2025/04/30
TI  - Slope Stability Prediction Based on GA-HIDMS-PSO-SVM
BT  - Proceedings of the 2024 6th International Conference on Civil Architecture and Urban Engineering (ICCAUE 2024)
PB  - Atlantis Press
SP  - 481
EP  - 491
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-688-8_49
DO  - 10.2991/978-94-6463-688-8_49
ID  - Zhang2025
ER  -