Науковий вісник НГУ

A hybrid ANN–FEM framework for high accuracy slope stability prediction

• 
• 

User Rating:  / 0

PoorBest 

Details

Category: Content №4 2026

Last Updated on 24 August 2026

Published on 30 November -0001

Hits: 1088

Authors:

F. Benayoun, orcid.org/0000-0002-7947-8861, University of Larbi Ben Mhidi,Urban Techniques Man-agement Institute, LDDPE Laboratory, Oum El Bouaghi, ­Algeria

M. Feligha, orcid.org/0000-0002-9879-6304, University 20 Aout 1955, Faculty of Technology, Department of Civil Engineering, LMGHU Laboratory, Skikda, Algeria

S. Rehab Bekkouche*, orcid.org/0009-0002-2100-9058, University 20 Aout 1955, Faculty of Technology, Department of Civil Engineering, LMGHU Laboratory, Skikda, Algeria, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

D. Boumezerane, orcid.org/0000-0002-4429-9687, University of the West of Scotland, Division of Engineering and Physical Sciences, Paisley, United Kingdom

M. Moussaoui, orcid.org/0000-0002-4902-7576, Badji Mokhtar-Annaba University, Faculty of Technology, Department of hydraulic, Annaba, Algeria

* Corresponding author e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

повний текст / full article

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2026, (4): 130 - 138

https://doi.org/10.33271/nvngu/2026-4/130

Abstract:

Purpose. To develop a hybrid methodology that integrates Artificial Neural Networks (ANN) with Finite Element Method (FEM) simulations for the rapid and accurate prediction of slope stability.

Methodology. A dataset of 1,000 FEM simulations was generated by systematically varying seven key input parameters: slope geometry (height and angle) and soil properties (cohesion, friction angle, unit weight, pore water pressure ratio, and reinforcement type). An ANN model with a (7-10-1) feedforward architecture was trained on this data.

Findings. The model demonstrated exceptional predictive performance, achieving a near-perfect correlation coefficient (R  0.999997) and an extremely low mean squared error (MSE = 3.6828  10^-6), showing close agreement with the FEM-computed factors of safety (FOS). A comprehensive sensitivity analysis based on analysis of variance identified the pore water pressure ratio as the dominant controlling parameter, contributing approximately 77 % to the variability of FOS, followed by cohesion with a contribution of about 13 %. Complementary correlation analysis revealed that cohesion exhibits the strongest linear correlation with FOS (r = 0.83), whereas the pore water pressure ratio shows a relatively weak linear correlation, highlighting its pronounced nonlinear and interaction-driven influence on slope stability. These results demonstrate that the proposed ANN–FEM hybrid framework provides a powerful, efficient, and reliable tool for slope stability assessment and parametric analysis. The methodology is particularly well suited for engineering applications requiring rapid decision-making, large-scale evaluations, and uncertainty analysis.

Originality. The core originality of this research is its development of a robust ANN–FEM hybrid framework applied to a large, systematically generated dataset of 1,000 slope simulations. Unlike many studies, it comprehensively incorporates seven critical input variables, including the often underrepresented pore water pressure. Furthermore, its scientific rigor is enhanced by a dual interpretability strategy that combines analysis of variance for quantifying parameter contribution and correlation heatmaps for distinguishing linear effects from nonlinear ones, providing deeper insight into slope stability mechanisms.

Practical value. This study provides engineers with a fast and reliable tool to predict slope safety in seconds instead of running time-consuming FEM simulations, making it highly valuable for real-time decision-making and large parametric studies. Practically, it also shows that controlling pore water pressure (through drainage) is the most effective risk-reduction strategy, while cohesion offers a predictable way to improve slope stability in design.

Keywords: slope stability, correlation analysis, finite element method (FEM), artificial neural network (ANN)

References.

1. Verma, A.K., Singh, T.N., Chauhan, N.K., & Sarkar, K. (2016). A hybrid FEM–ANN approach for slope instability prediction. Journal of The Institution of Engineers (India), Series A, 97(3), 171-180. https://doi.org/10.1007/s40030-016-0168-9

2. Chakraborty, A., & Goswami, D. (2017). Slope stability prediction using artificial neural network (ANN). International Journal of Engineering and Computer Science, 6(6), 21845-21848. https://doi.org/10.18535/ijecs/v6i6.49

3. Chakraborty, A., & Goswami, D. (2017). Prediction of slope stability using multiple linear regression (MLR) and artificial neural network (ANN). Arabian Journal of Geosciences, 10, 385. https://doi.org/10.1007/s12517-017-3167-x

4. Pandey, V. H. R., Kainthola, A., Sharma, V., Srivastav, A., ­Jayal, T., & Singh, T. N. (2022). Deep learning models for large-scale slope instability examination in Western Uttarakhand, India. Environmental Earth Sciences, 81, 487. https://doi.org/10.1007/s12665-022-10590-8

5. Huang, F., Xiong, H., Chen, S., Lv, Z., Huang, J., Chang, Z., & Catani, F. (2023). Slope stability prediction based on a long short-term memory neural network: comparisons with convolutional neural networks, support vector machines and random forest models. International Journal of Coal Science & Technology, 10, 18. https://doi.org/10.1007/s40789-023-00579-4

6. Khajehzadeh, M., Taha, M.R., Keawsawasvong, S., Mirzaei, H., & Jebeli, M. (2022). An effective artificial intelligence approach for slope stability evaluation. IEEE Access, 10, 5660-5671. https://doi.org/10.1109/ACCESS.2022.3141432

7. Kasa, A., & Mohd, S.F. (2024). Performance Prediction Evaluation of Machine Learning Models for Slope Stability Analysis: A Comparison Between ANN, ANN-ICA and ANFIS. Journal of Electrical Systems, 20, 4364-4374.

8. Ray, A., Kumar, V., Kumar, A., Rai, R., Khandelwal, M., & Singh, T. N. (2020). Stability prediction of Himalayan residual soil slope using artificial neural network. Natural Hazards, 103, 3523-3540. https://doi.org/10.1007/s11069-020-04141-2

9. Zhang, M., & Wei, J. (2025). Analysis of Slope Stability Based on Four Machine Learning Models: An Example of 188 Slopes. Periodica Polytechnica Civil Engineering. https://doi.org/10.3311/PPci.37630

10.      Wang, M. X., Leung, Y. F., & Li, D. Q. (2024). Neural network-assisted generic predictive models of safety factor and yield acceleration for seismic slope stability and displacement assessments. Canadian Geotechnical Journal, 62, 1-19. https://doi.org/10.1139/cgj-2024-0106

11.      Dutta, A., & Sarkar, K. (2024). A neural network model for predicting stability of jointed rock slopes against planar sliding. Journal of Earth System Science, 133, 201. https://doi.org/10.1007/s12040-024-02418-9

12.      Zhang, H., Nguyen, H., Bui, X.-N., Pradhan, B., Asteris, P. G., Costache, R., & Aryal, J. (2022). A generalized artificial intelligence model for estimating the friction angle of clays in evaluating slope stability using a deep neural network and Harris Hawks optimization algorithm. Engineering with Computers, 38(Suppl 5), 3901-3914. https://doi.org/10.1007/s00366-020-01272-9

13.      Salmasi, F., & Jafari, F. A. (2019). Simple Direct Method for Prediction of Safety Factor of Homogeneous Finite Slopes. Geotechnical and Geological Engineering, 37, 3949-3959. https://doi.org/10.1007/s10706-019-00884-3

14.      Alam, S. (2024). Artificial Neural Network Modelling for Slope Stability Analysis of Slopes Stabilized with Piles Using Levenberg-Marquardt Algorithm. Proceedings of the International Conference on Innovation and Entrepreneurship in Computing, Engineering and Science Education, (pp. 87). Springer Nature. https://doi.org/10.2991/978-94-6463-589-8_10

15.      Lei, D., Zhang, Y., Lu, Z., Lin, H., Fang, B., & Jiang, Z. (2024). Slope stability prediction using principal component analysis and hybrid machine learning approaches. Applied Sciences, 14(15), 6526. https://doi.org/10.3390/app14156526

• < Prev
• Next >