Abstract
Because of the disasters associated with slope failure, the analysis and forecasting of slope stability for geotechnical engineers are crucial. In this work, in order to forecast the factor of safety (FOS) of the slopes, six machine learning techniques of Gaussian process regression (GPR), support vector regression, decision trees, long-short term memory, deep neural networks, and K-nearest neighbors were performed. A total of 327 slope cases in Iran with various geometric and shear strength parameters analyzed by PLAXIS software to evaluate their FOS were employed in the models. The K-fold (K = 5) cross-validation (CV) method was applied to evaluate the performance of models’ prediction. Finally, all the models produced acceptable results and almost close to each other. However, the GPR model with R2 = 0.8139, RMSE = 0.160893, and MAPE = 7.209772% was the most accurate model to predict slope stability. Also, the backward selection method was applied to evaluate the contribution of each parameter in the prediction problem. The results showed that all the features considered in this study have significant contributions to slope stability. However, features φ (friction angle) and γ (unit weight) were the most effective and least effective parameters on slope stability, respectively.
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References
Bye AR, Bell FG (2011) Stability assessment and slope design at Sandsloot open pit, South Africa. Int J Rock Mech Mining Sci 38(3):449–466. https://doi.org/10.1016/S1365-1609(01)00014-4
Choobbasti A, Farrokhzad F, Barari A (2009) Prediction of slope stability using artificial neural network (case study: Noabad, Mazandaran, Iran). Arab J Geosci 2(4):311–319. https://doi.org/10.1007/S12517-009-0035-3
Das SK, Biswal RK, Sivakugan N, Das B (2011) Classification of slopes and prediction of factor of safety using differential evolution neural networks. Environ Earth Sci 64(1):201–210. https://doi.org/10.1007/S12665-010-0839-1
Duncan JM (2000) Factors of safety and reliability in geotechnical engineering. J Geotech Geoenviron Eng 126(4):307–316. https://doi.org/10.1061/(ASCE)1090-0241(2000)126:4(307)
Erzin Y, Cetin T (2013) The prediction of the critical factor of safety of homogeneous finite slopes using neural networks and multiple regressions. Comput Geosci 51:305–313. https://doi.org/10.1016/j.cageo.2012.09.003
Feng X (2000) Introduction of intelligent rock mechanics. Science Press, Beijing, China, pp 239–241
Feng X, Li S, Yuan C, Zeng P, Sun Y (2018) Prediction of slope stability using Naive Bayes classifier. KSCE J Civ Eng 22:941–950. https://doi.org/10.1007/s12205-018-1337-3
Gordan B, Jahed-Armaghani D, Hajihassani M, Monjezi M (2016) Prediction of seismic slope stability through combination of particle swarm optimization and neural network. Eng Comput 32(1):85–97. https://doi.org/10.1007/s00366-015-0400-7
He L, Wu G, Wang H (2012) Study of base friction simulation tests based on a complicated engineered bridge slope. Front Struct Civ Eng 6:393–397. https://doi.org/10.1007/s11709-012-0174-6
Hoang ND, Pham AD (2016) Hybrid artificial intelligence approach based on metaheuristic and machine learning for slope stability assessment: A multinational data analysis. Expert Syst Appl 46:60–68. https://doi.org/10.1016/j.eswa.2015.10.020
Khajehzadeh M, Taha MR, El-Shafie A, Eslami M (2012) A modified gravitational search algorithm for slope stability analysis. Eng Appl Artif Intell 25:1589–1597. https://doi.org/10.1016/j.engappai.2012.01.011
Kim JC, Jung H, Kim S, Chung K (2016) Slope based intelligent 3D disaster simulation using physics engine. Wireless Pers Commun 86:183–199. https://doi.org/10.1007/s11277-015-2788-1
Li S, Zhao HB, Ru Z (2013) Slope reliability analysis by updated support vector machine and Monte Carlo simulation. Nat Hazards 65:707–722. https://doi.org/10.1007/s11069-012-0396-x
Li X (2004) Comparative studies of artificial neural networks and adaptive Neuro-Fuzzy inference system based approach for the circular sliding slopes stability analysis, Master Thesis, University of South China, Hengyang, Hunan, China.
Lin Y, Zhou K, Li J (2018) Prediction of slope stability using four supervised learning methods. In IEEE Access 6:31169–31179. https://doi.org/10.1109/ACCESS.2018.2843787
Liu Z, Shao J, Xu W, Chen H, Zhang Y (2014) An extreme learning machine approach for slope stability evaluation and prediction. Nat Hazards 73(2):787–804. https://doi.org/10.1007/s11069-014-1106-7
Liu Z, Shao JF, Weiya X, Wu Q (2015) Indirect estimation of unconfined compressive strength of carbonate rocks using extreme learning machine. Acta Geotech 10(5):651–663
Liu Z, Shao J, Xu W, Shi C (2013) Estimation of elasticity of porous rock based on mineral composition and microstructure. Adv Mater Sci Eng, 2013, Article ID 512727, 10 pages. https://doi.org/10.1155/2013/512727
Lu P, Rosenbaum MS (2003) Artificial neural networks and grey systems for the prediction of slope stability. Nat Hazards 30(3):383–398. https://doi.org/10.1023/B:NHAZ.0000007168.00673.27
Mahmoodzadeh A, Zare S (2016) Probabilistic prediction of expected Ground conditions and construction time and costs in road tunnels. J Rock Mech Geotech Eng 8:734–745. https://doi.org/10.1016/j.jrmge.2016.07.001
Mahmoodzadeh A, Mohammadi M, Daraei A, Rashid TA, Sherwani AFH, Faraj RH, Darwesh AM (2019) Updating ground conditions and time-cost scatter-gram in tunnels during excavation. Autom Constr 105:102822. https://doi.org/10.1016/j.autcon.2019.04.017
Mahmoodzadeh A, Mohammadi M, Daraei A, Faraj RH, Omer RMD, Sherwani AFH (2020a) Decision-making in tunneling using artificial intelligence tools. Tunn Undergr Space Technol 103:103514. https://doi.org/10.1016/j.tust.2020.103514
Mahmoodzadeh A, Mohammadi M, Daraei A, Hama-Ali HF, Abdullah AI, Al-Salihi NK (2020b) Forecasting tunnel geology, construction time and costs using machine learning methods. Neural Comput Appl 33:321–348. https://doi.org/10.1007/s00521-020-05006-2
Mahmoodzadeh A, Mohammadi M, Daraei A, Hama-Ali HF, Al-Salihi NK, Omer RMD (2020c) Forecasting maximum surface settlement caused by urban tunneling. Autom Constr 120:103375. https://doi.org/10.1016/j.autcon.2020.103375
Mahmoodzadeh A, Mohammadi M, Abdulhamid SN, Ibrahim HH, Hama-Ali HF, Salim SG (2021a) Dynamic reduction of time and cost uncertainties in tunneling projects. Tunn Undergr Space Technol 109:103774. https://doi.org/10.1016/j.tust.2020.103774
Mahmoodzadeh A, Mohammadi M, Abdulhamid SN, Nejati HR, Noori KMG, Ibrahim HH, Hama-Ali HF (2021b) Predicting construction time and cost of tunnels using Markov chain model considering opinions of experts. Tunn Undergr Space Technol 116:104109. https://doi.org/10.1016/j.tust.2021.104109
Mahmoodzadeh A, Mohammadi M, Ibrahim HH, Abdulhamid SN, Salim SG, Hama-Ali HF, Majeed MK (2021c) Artificial intelligence forecasting models of uniaxial compressive strength. Transp Geotech 27:100499. https://doi.org/10.1016/j.trgeo.2020.100499
Mahmoodzadeh A, Mohammadi M, Hama-Ali HF, Abdulhamid SN, Ibrahim HH, Noori KMG (2021d) Dynamic prediction models of rock quality designation in tunneling projects. Transp Geotech 27:100497. https://doi.org/10.1016/j.trgeo.2020.100497
Mahmoodzadeh A, Mohammadi M, Daraei A, Hama-Ali HF, Abdullah AI, Al-Salihi NK (2021e) Forecasting tunnel geology, construction time and costs using machine learning methods. Neural Comput Appl 33:321–348. https://doi.org/10.1007/s00521-020-05006-2
Mahmoodzadeh A, Mohammadi M, Ibrahim HH, Abdulhamid SN, Ham-Ali HF, Hasan AM, Khishe M, Mahmud H (2021f) Machine learning forecasting models of disc cutters life of tunnel boring machine. Autom Constr 128:103779. https://doi.org/10.1016/j.autcon.2021.103779
Mahmoodzadeh A, Mohammadi M, Noori KMG, Khishe M, Ibrahim HH, Hama-Ali HF, Abdulhamid SN (2021g) Presenting the best prediction model of water inflow into drill and blast tunnels among several machine learning techniques. Autom Constr 127:103719. https://doi.org/10.1016/j.autcon.2021.103719
Mahmoodzadeh A, Mohammadi M, Ibrahim HH, Noori KMG, Abdulhamid SN, Hama-Ali HF (2021h) Forecasting sidewall displacement of underground caverns using machine learning techniques. Autom Constr 123:103530. https://doi.org/10.1016/j.autcon.2020.103530
Mahmoodzadeh A, Mohammadi M, Ibrahim HH, Rashid TA, Aldalwie AHM, Hama-Ali HF, Daraei A (2021i) Tunnel geomechanical parameters prediction using Gaussian process regression. Mach Learn Appl 3:100020. https://doi.org/10.1016/j.mlwa.2021.100020
Manouchehrian A, Gholamnejad J, Sharifzadeh M (2014) Development of a model for analysis of slope stability for circular mode failure using genetic algorithm. Environ Earth Sci 71:1267–1277. https://doi.org/10.1007/s12665-013-2531-8
Pirone M, Papa R, Nicotera MV, Urciuoli G (2015) In situ monitoring of the groundwater field in an unsaturated pyroclastic slope for slope stability evaluation. Landslides 12:259–276. https://doi.org/10.1007/s10346-014-0483-z
Raihan TM, Mohammad K, Mahdiyeh E (2013) A new hybrid algorithm for global optimization and slope stability evaluation. J Central South Univ 20:3265–3273. https://doi.org/10.1007/s11771-013-1850-y
Rukhaiyar S, Alam M, Samadhiya N (2017) A PSO-ANN hybrid model for predicting factor of safety of slope. Int J Geotech Eng 12(6):556–566. https://doi.org/10.1080/19386362.2017.1305652
Sakellariou MG, Ferentinou MD (2005) A study of slope stability prediction using neural networks. Geotech Geol Eng 23:419–445. https://doi.org/10.1007/s10706-004-8680-5
Samui P (2008) Slope stability analysis: A support vector machine approach. Environ Geol 56(2):255–267. https://doi.org/10.1007/s00254-007-1161-4
Sarkar K, Singh TN, Verma AK (2012) A numerical simulation of landslide-prone slope in Himalayan region—a case study. Arab J Geosci 5:73–81. https://doi.org/10.1007/s12517-010-0148-8
Shi XZ, Zhou J, Zheng W, Hu HY, Wang HY (2010) Bayes discriminant analysis method and its application for prediction of slope stability. Jsichuan Univ Eng Sci Ed 42(3):63–68
Suman S, Khan SZ, Das SK, Chand SK (2016a) Slope stability analysis using artificial intelligence techniques. Nat Hazards 84:727–748. https://doi.org/10.1007/s11069-016-2454-2
Suman S, Khan S, Das S, Chand S (2016b) Slope stability analysis using artificial intelligence techniques. Nat Hazards 84(2):727–748. https://doi.org/10.1007/s11069-016-2454-2
Thiebes B, Bell R, Glade T, Jäger S, Anderson M, Holcombe L (2013) A WebGIS decision-support system for slope stability based on limit-equilibrium modelling. Eng Geol 158:109–118. https://doi.org/10.1016/j.enggeo.2013.03.004
Thiebes B, Bell R, Glade T, Jäger S, Mayer J, Anderson M, Holcombe L (2014) Integration of a limit-equilibrium model into a landslide early warning system. Landslides 11:859–875. https://doi.org/10.1007/s10346-013-0416-2
Trivedi R, Vishal V, Pradhan S, Singh T, Jhanwar J (2012) Slope stability analysis in limestone mines. Int J Earth Sci Eng 5:759–766
Vapnik VN (1995) The nature of statistical learning theory. Springer, New York
Verma A, Singh T, Chauhan NK, Sarkar K (2016) A hybrid FEM–ANN approach for slope instability prediction. J Instit Engs India Ser A, 97(3):171–180. https://doi.org/10.1007/s40030-016-0168-9
Wang H, Xu W, Xu R (2005) Slope stability evaluation using back propagation neural networks. Eng Geol 80(3):302–315. https://doi.org/10.1016/j.enggeo.2005.06.005
Wen T, Zhang B (2014) Prediction model for open-pit coal mine slope stability based on random forest. Sci Technol Rev 32(4/5):105–109
Xue X (2017) Prediction of slope stability based on Hybrid PSO and LSSVM. J Comput Civ Eng 31(1):1–10. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000607
Xue X, Yang X, Chen X (2014) Application of a support vector machine for prediction of slope stability. Sci China Technol Sci 57:2379–2386. https://doi.org/10.1007/s11431-014-5699-6
Youssef AM, Pourghasemi HR, Pourtaghi ZS, Al-Katheeri MM (2016) Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia. Landslides 13:839–856. https://doi.org/10.1007/s10346-015-0614-1
Zhang Z, Liu Z, Zheng L, Zhang Y (2014) Development of an adaptive relevance vector machine approach for slope stability inference. Neural Comput Appl 25:2025–2035. https://doi.org/10.1007/s00521-014-1690-1
Zhao HB (2008) Slope reliability analysis using a support vector machine. Comput Geotech 35(3):459–467. https://doi.org/10.1016/j.compgeo.2007.08.002
Zhao H, Yin S, Ru Z (2012) Relevance vector machine applied to slope stability analysis. Int J Numer Anal Methods Geomech 36(5):643–652. https://doi.org/10.1002/nag.1037
Zhiquan H, Jiangli C, Handong L (2004) Chaotic neural network method for slope stability prediction. Chinese J Rock Mech Eng 22
Zhou J, Li X (2016) Mitri HS (2016) Classification of Rockburst in Underground Projects: Comparison of Ten Supervised Learning Methods. J Comput Civil Eng. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000553
Zhou J, Li E, Yang S, Wang M, Shi X, Yao S, Mitri HS (2019) Slope stability prediction for circular mode failure using gradient boosting machine approach based on an updated database of case histories. Saf Sci 118:505–518. https://doi.org/10.1016/j.ssci.2019.05.046
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Mahmoodzadeh, A., Mohammadi, M., Farid Hama Ali, H. et al. Prediction of safety factors for slope stability: comparison of machine learning techniques. Nat Hazards 111, 1771–1799 (2022). https://doi.org/10.1007/s11069-021-05115-8
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DOI: https://doi.org/10.1007/s11069-021-05115-8