Mining of Mineral Deposits

ISSN 2415-3443 (Online)

ISSN 2415-3435 (Print)

Flag Counter

Physics-guided ensemble machine learning framework for slope failure prediction

Bushra Mohamed Elamin Elnaim1, Mohammed Mnzool2

1Department of Computer Engineering & Information-Al-Sulail, College of Engineering in Wadi-Aldwaser, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia

2Department of Civil Engineering, College of Engineering, Taif University, Taif, Saudi Arabia


Min. miner. depos. 2026, 20(2):101-110


https://doi.org/10.33271/mining20.02.101

Full text (PDF)


      ABSTRACT

      Purpose. This research aims to provide a physics-based ensemble machine learning framework that can reliably predict slope stability and distinguish stable from unstable slopes in static and seismic conditions. Standard analytical and numerical methods have significant processing costs and oversimplified assumptions that limit their usefulness.

      Methods. The study analyzed 700 slope stability samples, including geotechnical and seismic factors such as slope height, slope angle, cohesion, internal friction angle, and peak ground acceleration. The proposed model now includes physics-based engineering elements, such as tan ϕ, c/H, and PGA/g, to account for geotechnical interactions. A linear meta-learner and Random Forest and Gradient Boosting regressors were used to develop a stacked ensemble framework. We assessed model strength and reliability.

      Findings. The devised framework showcased exceptional prediction performance with an (R2) of 0.982, a mean absolute error of 0.02 and a root mean square error of nearly 0.03. Cross-validation showed consistent generalization. Random Forests classified slope stability conditions with 97.4% accuracy. The most important parameters for slope stability predictions were the internal friction angle and cohesiveness. Furthermore, the inclusion of carefully crafted physics-based features demonstrably improved robustness, accuracy and consistency.

      Originality. This work introduces a pioneering, combined framework that fuses the mechanics of geotechnical soil with ensemble machine learning techniques, enhancing both interpretability and the certainty of slope stability predictions.

      Practical implications. The framework may support rapid, cost-effective, and interpretable preliminary geotechnical risk assessment and design screening. However, its use in slope monitoring or early-warning applications requires further field validation and integration with monitoring data.

      Keywords: physics-guided machine learning; slope stability; factor of safety; ensemble learning; seismic loading


      REFERENCES

  1. Aalianvari, A., & Jahanmiri, S. (2025). A comparative study of TabNet and classical machine learning models for landslide prediction. Research Square. Preprint. https://doi.org/10.21203/rs.3.rs-7694229/v1
  2. Trinidad, M., & Momayez, M. (2025). Machine learning in slope stability: A review with implications for landslide hazard assessment. Geohazards, 6(4), 67. https://doi.org/10.3390/geohazards6040067
  3. Aminpour, M., Alaie, R., Kardani, N., Moridpour, S., & Nazem, M. (2022). Highly efficient reliability analysis of anisotropic heterogeneous slopes: Machine learning-aided Monte Carlo method. Acta Geotechnica, 18(6), 3367-3389. https://doi.org/10.1007/s11440-022-01771-7
  4. Zhang, Z., Liu, X., Wang, Y., Li, E., & Zhang, Y. (2024). Stability prediction model of transmission tower slope based on ISCSO-SVM. Electronics, 14(1), 126. https://doi.org/10.3390/electronics14010126
  5. Pei, T., Qiu, T., & Shen, C. (2023). Applying knowledge-guided machine learning to slope stability prediction. Journal of Geotechnical and Geoenvironmental Engineering, 149(10). https://doi.org/10.1061/jggefk.gteng-11053
  6. Yuan, B., Choo, C.S., Yeo, L.Y., Wang, Y., Yang, Z., Guan, Q., Suryasentana, S., Choo, J., Shen, H., Megia, M., Zhang, J., Liu, Z., Song, Y., Wang, H., & Chen, X. (2025). Physics-informed machine learning in geotechnical engineering: a direction paper. Geomechanics and Geoengineering, 20(5), 1128-1159. https://doi.org/10.1080/17486025.2025.2502029
  7. Zhang, Z., Wang, B., Li, Z., Ye, X., Sun, Z., & Dias, D. (2024). Physics-guided neural network-based framework for 3D modeling of slope stability. Computers and Geotechnics, 176, 106801. https://doi.org/10.1016/j.compgeo.2024.106801
  8. Asteris, P.G., Rizal, F.I.M., Koopialipoor, M., Roussis, P.C., Ferentinou, M., Armaghani, D.J., & Gordan, B. (2022). Slope stability classification under seismic conditions using several tree-based intelligent techniques. Applied Sciences, 12(3), 1753. https://doi.org/10.3390/app12031753
  9. Sun, Z., Wang, W., & Liu, H. (2025). Pseudo-static design and analysis of seismic earth pressure for cantilever retaining walls with limitation assessment. Designs, 9(5), 114. https://doi.org/10.3390/designs9050114
  10. Tavakoli, H., & Soleimani Kutanaei, S. (2014). Evaluation of effect of soil characteristics on the seismic amplification factor using the neural network and reliability concept. Arabian Journal of Geosciences, 8(6), 3881-3891. https://doi.org/10.1007/s12517-014-1458-z
  11. Waris, K.A., ur Rahaman, M.A., & Basha, B.M. (2025). Detection and prediction of slope stability in unsaturated finite slopes using interpretable machine learning. Modeling Earth Systems and Environment, 11(4). https://doi.org/10.1007/s40808-025-02454-4
  12. Mnzool, M., Ali, E., Elhassan, A.A.M., Bakri, M., Paramasivam, P., Yang, Y., & Elsayed, E. (2025). Assessing the efficacy of ensemble learning techniques to slope stability classification in mining operations. Mining, Metallurgy & Exploration, 42(4), 2281-2298. https://doi.org/10.1007/s42461-025-01271-8
  13. Lin, M., Zeng, L., Teng, S., Chen, G., & Hu, B. (2024). Prediction of stability of a slope with weak layers using convolutional neural networks. Natural Hazards, 120(13), 12081-12105. https://doi.org/10.1007/s11069-024-06674-2
  14. Zhou, S., Han, W., Huang, M., Xu, Z., Li, J., & Zhang, J. (2025). Slope stability prediction based on incremental learning Bayesian model and literature data mining. Applied Sciences, 15(5), 2423. https://doi.org/10.3390/app15052423
  15. Mahmoodzadeh, A., Mohammadi, M., Farid Hama Ali, H., Hashim Ibrahim, H., Nariman Abdulhamid, S., & Nejati, H. R. (2021). Prediction of safety factors for slope stability: Comparison of machine learning techniques. Natural Hazards, 111(2), 1771-1799. https://doi.org/10.1007/s11069-021-05115-8
  16. Ragam, P., Kushal Kumar, N., Ajith, J.E., Karthik, G., Himanshu, V.K., Sree Machupalli, D., & Ramesh Murlidhar, B. (2024). Estimation of slope stability using ensemble-based hybrid machine learning approaches. Frontiers in Materials, 11. https://doi.org/10.3389/fmats.2024.1330609
  17. Kurnaz, T.F., Erden, C., Dağdeviren, U., Demir, A.S., & Kökçam, A.H. (2024). Comparison of machine learning algorithms for slope stability prediction using an automated machine learning approach. Natural Hazards, 120(8), 6991-7014. https://doi.org/10.1007/s11069-024-06490-8
  18. Aminpour, M., Alaie, R., Khosravi, S., Kardani, N., Moridpour, S., & Nazem, M. (2023). Slope stability machine learning predictions on spatially variable random fields with and without factor of safety calculations. Computers and Geotechnics, 153, 105094. https://doi.org/10.1016/j.compgeo.2022.105094
  19. Vermeer, J.S. (2024). Machine learning aided reliability analysis for spatially varying slopes in 2D and 3D. Doctoral dissertation. Delft, The Netherlands: Delft University of Technology.
  20. Yadav, D.K., Chattopadhyay, S., Tripathy, D.P., Mishra, P., & Singh, P. (2025). Enhanced slope stability prediction using ensemble machine learning techniques. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-90539-6
  21. Yuan, C., & Moayedi, H. (2019). The performance of six neural-evolutionary classification techniques combined with multi-layer perception in two-layered cohesive slope stability analysis and failure recognition. Engineering with Computers, 36(4), 1705-1714. https://doi.org/10.1007/s00366-019-00791-4
  22. Zeng, F., Nait Amar, M., Mohammed, A.S., Motahari, M.R., & Hasanipanah, M. (2021). Improving the performance of LSSVM model in predicting the safety factor for circular failure slope through optimization algorithms. Engineering with Computers, 38(S3), 1755-1766. https://doi.org/10.1007/s00366-021-01374-y
  23. 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
  24. Rukhaiyar, S., & Samadhiya, N.K. (2017). Strength behaviour of sandstone subjected to polyaxial state of stress. International Journal of Mining Science and Technology, 27(6), 889-897. https://doi.org/10.1016/j.ijmst.2017.06.022
  25. Cai, F., & Ugai, K. (2004). Numerical analysis of rainfall effects on slope stability. International Journal of Geomechanics, 4(2), 69-78. https://doi.org/10.1061/(asce)1532-3641(2004)4:2(69)
  26. Mnzool, M. (2024). Prediction of rock slope failure based on multiple machine learning algorithms. Thermal Science, 28(6 Part B), 4907-4916. https://doi.org/10.2298/tsci2406907m
  27. Sakellariou, M.G., & Ferentinou, M.D. (2005). A study of slope stability prediction using neural networks. Geotechnical and Geological Engineering, 23(4), 419-445. https://doi.org/10.1007/s10706-004-8680-5
  28. Samui, P. (2008). Support vector machine applied to settlement of shallow foundations on cohesionless soils. Computers and Geotechnics, 35(3), 419-427. https://doi.org/10.1016/j.compgeo.2007.06.014
  29. Dawson, E.M., Roth, W.H., & Drescher, A. (1999). Slope stability analysis by strength reduction. Géotechnique, 49(6), 835-840. https://doi.org/10.1680/geot.1999.49.6.835
  30. Das, S.K., Biswal, R.K., Sivakugan, N., & Das, B. (2011). Classification of slopes and prediction of factor of safety using differential evolution neural networks. Environmental Earth Sciences, 64(1), 201-210. https://doi.org/10.1007/s12665-010-0839-1
  31. Choobbasti, A.J., Farrokhzad, F., & Barari, A. (2009). Prediction of slope stability using artificial neural network (case study: Noabad, Mazandaran, Iran). Arabian Journal of Geosciences, 2(4), 311-319. https://doi.org/10.1007/s12517-009-0035-3
  32. Zhao, H.-B. (2008). Slope reliability analysis using a support vector machine. Computers and Geotechnics, 35(3), 459-467. https://doi.org/10.1016/j.compgeo.2007.08.002
  33. Moayedi, H., & Hayati, S. (2018). Artificial intelligence design charts for predicting friction capacity of driven pile in clay. Neural Computing and Applications, 31(11), 7429-7445. https://doi.org/10.1007/s00521-018-3555-5
  34. Xue, X. (2017). Prediction of slope stability based on hybrid PSO and LSSVM. Journal of Computing in Civil Engineering, 31(1). https://doi.org/10.1061/(asce)cp.1943-5487.0000607
  35. Huang, Z., Cui, J., & Liu, H. (2004). Chaotic neural network method for slope stability prediction. Chinese Journal of Rock Mechanics and Engineering, 22, 015.
  36. Lu, P., & Rosenbaum, M.S. (2003). Artificial neural networks and grey systems for the prediction of slope stability. Natural Hazards, 30(3), 383-398. https://doi.org/10.1023/b:nhaz.0000007168.00673.27
  37. Sah, N.K., Sheorey, P.R., & Upadhyaya, L.N. (1994). Maximum likelihood estimation of slope stability. International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts, 31(1), 47-53. https://doi.org/10.1016/0148-9062(94)92314-0
  38. Li, J., & Wang, F. (2010). Study on the forecasting models of slope stability under data mining. Earth and Space 2010, 765-776. https://doi.org/10.1061/41096(366)77
  39. Mohammed, M., Wan, L., & Wei, Z. (2015). Slope stability analysis of Southern slope of Chengmenshan copper mine, China. International Journal of Mining Science and Technology, 25(2), 171-175. https://doi.org/10.1016/j.ijmst.2015.02.002
  40. Liu, Z., Shao, J., Xu, W., Chen, H., & Zhang, Y. (2014). An extreme learning machine approach for slope stability evaluation and prediction. Natural Hazards, 73(2), 787-804. https://doi.org/10.1007/s11069-014-1106-7
  41. Gordan, B., Jahed Armaghani, D., Hajihassani, M., & Monjezi, M. (2015). Prediction of seismic slope stability through combination of particle swarm optimization and neural network. Engineering with Computers, 32(1), 85-97. https://doi.org/10.1007/s00366-015-0400-7
  42. Kaliappan, S., Natrayan, L., Kumar, P.V.A., & Raturi, A. (2023). Mechanical, fatigue, and hydrophobic properties of silane-treated green pea fiber and egg fruit seed powder epoxy composite. Biomass Conversion and Biorefinery, 14(19), 24061-24068. https://doi.org/10.1007/s13399-023-04534-w
  43. Zhang, X., Yang, L., Xiao, C., Li, J., Hu, T., & Li, L. (2024). Association between waist-to-hip ratio and risk of myocardial infarction: A systematic evaluation and meta-analysis. Frontiers in Cardiovascular Medicine, 11. https://doi.org/10.3389/fcvm.2024.1438817
  44. Yuan, C., & Moayedi, H. (2019). Evaluation and comparison of the advanced metaheuristic and conventional machine learning methods for the prediction of landslide occurrence. Engineering with Computers, 36(4), 1801-1811. https://doi.org/10.1007/s00366-019-00798-x
  45. Lin, Y., Zhou, K., & Li, J. (2018). Prediction of slope stability using four supervised learning methods. IEEE Access, 6, 31169-31179. https://doi.org/10.1109/access.2018.2843787
  46. Wen, C., Huai, T., Zhang, Q., Song, Z., & Cao, F. (2022). A new rotation forest ensemble algorithm. International Journal of Machine Learning and Cybernetics, 13(11), 3569-3576. https://doi.org/10.1007/s13042-022-01613-8
  47. Abiye, W., & Dengiz, O. (2025). Digital mapping of soil erodibility factor in response to land use change using machine learning models. Environmental Systems Research, 14(1). https://doi.org/10.1186/s40068-025-00402-w
  48. Oláh, P., & Görög, P. (2025). Integrating soil parameter uncertainty into slope stability analysis: A case study of an open pit mine in Hungary. Geosciences, 15(6), 222. https://doi.org/10.3390/geosciences15060222

    49. Лицензия Creative Commons

Copyright © 2026 Dnipro University of Technology

Underground Mining Department

All Rights Reserved.

Journal homepage Authors