A Review of Slope Stability Analysis Using Artificial Neural Networks

Review: Analisis Kestabilan Lereng Menggunakan Artificial Neural Network

  • Risaldi Hidayat Universitas Pembangunan Nasional "Veteran" Yogyakarta


Over the last few years, arificial neural network (ANN) have been used successfully for modeling almost all aspect of geotechnical engineering problems especially in slope stability. Based on the comparison in this paper, that ANN has many advantages if the problem cannot be solved by mathematically and handle large data set. There are various intelligent algorithms available, therefore ANN is not a new concept. However, ANN's ability to solve complex geotechnical engineering problems (such as, which is found within the slope stability analysis) is its main advantage. So, this paper to provide an overview of ANN modeling in slope stability analysis as part of geotechnical engineering problems and research direction of ANN that needs further attention in the future.


[1] Surjandari, N. S., Djajaputra, A. A., & RW, S. P. (2010). Artificial Neural Network Model for Analysis Ultimit Bearing Capacity of Single Pile.
[2] Weiya XU and J.-F. SHAO. (1997). Application of ANNs in the rock slope engineering. Journal of University of Hydraulic and Electric Engineering. Vol 20: 9-20.
[3] Sulewska, M. J. (2017). Applying artificial neural networks for analysis of geotechnical problems. Computer Assisted Methods in Engineering and Science.18(4): 231-241.
[4] Haykin, S., Vanneschi, L., and Castelli, M. (2018). Neural Networks and Learning Machines Third Edition. In Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics: Vols. 1 – 3.
[5] Shahin, M. A., Jaksa, M. B., and Maier, H. R. (2001). Artificial neural network applications in geotechnical engineering. Australian Geomechanics. 36(1): 49-62.
[6] Tokar, S. A., and Johnson, P. A. (1999). Rainfall-runoff modeling using artificial neural networks. Journal of Hydrologic Engineering. 4(3): 232-239.
[7] Xu, W., & Shao, J. F. (1998). Artificial neural network analysis for the evaluation of slope stability. In Application of Numerical Methods to Geotechnical Problems. Springer. 665-672.
[8] Lu, P., & Rosenbaum, M. S. (2003). Artificial neural networks and grey systems for the prediction of slope stability. Natural Hazards. 30(3): 383-398.
[9] Sakellariou, M. G., & Ferentinou, M. D. (2005). A study of slope stability prediction using neural networks. Geotechnical & Geological Engineering. 23(4): 419-445.
[10] Shangguan, Z., Li, S., & Luan, M. (2009). Intelligent forecasting method for slope stability estimation by using probabilistic neural networks. Electron J Geotech Eng Bundle. Vol 13.
[11] 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.
[12] Ahangar‐Asr, A., Faramarzi, A., & Javadi, A. A. (2010). A new approach for prediction of the stability of soil and rock slopes. Engineering Computations.
[13] Liang, H., & Zhang, H. (2010). Identification of slope stability based on the contrast of BP neural network and SVM. In 2010 3rd International Conference on Computer Science and Information Technology. Vol. 9: 347-350.
[14] Jianping, J. (2011, August). BP neural networks for prediction of the factor of safety of slope stability. In 2nd International Conference on Computing, Control and Industrial Engineering. Vol. 2: 337-340.
[15] 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.
[16] Cetina, T. (2014). The prediction of the critical factor of safety of homogeneous finite slopes subjected to earthquake forces using neural networks and multiple regressions. Geomech Eng. 6(1): 1-15.
[17] Gelisli, K., Kaya, T., & Babacan, A. E. (2015). Assessing the factor of safety using an artificial neural network: case studies on landslides in Giresun, Turkey. Environmental Earth Sciences. 73(12): 8639-8646.
[18] Khandelwal, M., Rai, R., & Shrivastva, B. K. (2015). Evaluation of dump slope stability of a coal mine using artificial neural network. Geomechanics and Geophysics for Geo-energy and Geo-resources. 1(3): 69-77.
[19] Abdalla, J. A., Attom, M. F., & Hawileh, R. (2015). Prediction of minimum factor of safety against slope failure in clayey soils using artificial neural network. Environmental Earth Sciences. 73(9): 5463-5477.
[20] Kostić, S., Vasović, N., Todorović, K., & Samčović, A. (2016). Application of artificial neural networks for slope stability analysis in geotechnical practice. In 13th Symposium on Neural Networks and Applications. pp. 1-6.
[21] Chakraborty, A., & Goswami, D. (2017). Prediction of slope stability using multiple linear regression (MLR) and artificial neural network (ANN). Arabian Journal of Geosciences. 10(17): 1-11.
[22] Chakraborty, A., & Goswami, D. (2017). Slope stability prediction using artificial neural network (ANN). International Journal of Engineering and Computer Science. 6(6): 21845-21848.
[23] Kumar, S., & Basudhar, P. K. (2018). A neural network model for slope stability computations. Géotechnique Letters. 8(2): 149-154.
[24] Li, A. J., Lim, K., Chiu, C. K., & Hsiung, B. (2018). Application of artificial neural network in assessing fill slope stability. International Journal of Geotechnical and Geological Engineering. 12(2): 73-77.
[25] Ferentinou, M., & Fakir, M. (2018). Integrating rock engineering systems device and artificial neural networks to predict stability conditions in an open pit. Engineering Geology. 246: 293-309.
[26] Qian, Z. G., Li, et.al. (2019). An artificial neural network approach to inhomogeneous soil slope stability predictions based on limit analysis methods. Soils and foundations. 59(2): 556-569.
[27] Tien Bui, D., Moayedi, H., Gör, M., Jaafari, A., & Foong, L. K. (2019). Predicting slope stability failure through machine learning paradigms. ISPRS International Journal of Geo-Information. 8(9): 395.
[28] Majeed, M. Q., Hussein, M. K., & Mohammed, M. (2019). Slope Stability Prediction of Homogenous Earth Dam Caused by Fluid Particles Seeps by Using Artificial Neural Networks. Journal of Advanced Research in Fluid Mechanics and Thermal Sciences. 63(2): 295-301.
[29] Liao, Z., & Liao, Z. (2020). Slope stability evaluation using backpropagation neural networks and multivariate adaptive regression splines. Open Geosciences. 12(1): 1263-1273.
[30] 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(3): 3523-3540.
[31] Omar, M., Che Mamat, R., Abdul Rasam, A. R., Ramli, A., & Samad, A. (2021). Artificial intelligence application for predicting slope stability on soft ground: A comparative study. Int. J. Adv. Technol. Eng. Explor. 8: 362-370.
[32] Marrapu, B. M., Kukunuri, A., & Jakka, R. S. (2021). Improvement in Prediction of Slope Stability & Relative Importance Factors Using ANN. Geotechnical and Geological Engineering. 39(8): 5879-5894.
[33] Moayedi, H. (2021). Two novel predictive networks for slope stability analysis using a combination of genetic programming and artificial neural network techniques. In Proceedings of the International Conference on Innovations for Sustainable and Responsible Mining. Springer. pp. 91-108.
[34] Bharati, A. K., Ray, A., Khandelwal, M., Rai, R., & Jaiswal, A. (2022). Stability evaluation of dump slope using artificial neural network and multiple regression. Engineering with Computers. 38(3): 1835-1843.
[35] Khajehzadeh, M., Taha, M. R., Keawsawasvong, S., Mirzaei, H., & Jebeli, M. (2022). An effective artificial intelligence approach for slope stability evaluation. 10: 5660-5671.
[36] Mamata, R. C., et.al. (2022). Slope Stability Prediction of Road Embankment using Artificial Neural Network Combined with Genetic Algorithm. Jurnal Kejuruteraan. 34(1): 165-173.
How to Cite
Risaldi Hidayat (2022) “A Review of Slope Stability Analysis Using Artificial Neural Networks”, ReTII, pp. 209-215. Available at: //journal.itny.ac.id/index.php/ReTII/article/view/3610 (Accessed: 22June2024).