Geomechanics and Engineering

  • 제31권5호
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  • Pages.453-460
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  • 2022
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  • 2005-307X(pISSN)
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  • 2092-6219(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Gaussian process regression model to predict factor of safety of slope stability

  • Arsalan, Mahmoodzadeh (Rock Mechanics Division, School of Engineering, Tarbiat Modares University) ;
  • Hamid Reza, Nejati (Rock Mechanics Division, School of Engineering, Tarbiat Modares University) ;
  • Nafiseh, Rezaie (Department of Civil Engineering, Faculty of Engineering, University of Qom) ;
  • Adil Hussein, Mohammed (Department of Communication and Computer Engineering, Faculty of Engineering, Cihan University-Erbil) ;
  • Hawkar Hashim, Ibrahim (Department of Civil Engineering, College of Engineering, Salahaddin University-Erbil) ;
  • Mokhtar, Mohammadi (Department of Information Technology, College of Engineering and Computer Science, Lebanese French University) ;
  • Shima, Rashidi (Department of Computer Science, College of Science and Technology, University of Human Development)
  • 투고 : 2021.06.28
  • 심사 : 2022.11.15
  • 발행 : 2022.12.10
⟨ 이전 논문 다음 논문 ⟩

초록

It is essential for geotechnical engineers to conduct studies and make predictions about the stability of slopes, since collapse of a slope may result in catastrophic events. The Gaussian process regression (GPR) approach was carried out for the purpose of predicting the factor of safety (FOS) of the slopes in the study that was presented here. The model makes use of a total of 327 slope cases from Iran, each of which has a unique combination of geometric and shear strength parameters that were analyzed by PLAXIS software in order to determine their FOS. The K-fold (K = 5) technique of cross-validation (CV) was used in order to conduct an analysis of the accuracy of the models' predictions. In conclusion, the GPR model showed excellent ability in the prediction of FOS of slope stability, with an R2 value of 0.8355, RMSE value of 0.1372, and MAPE value of 6.6389%, respectively. According to the results of the sensitivity analysis, the characteristics (friction angle) and (unit weight) are, in descending order, the most effective, the next most effective, and the least effective parameters for determining slope stability.

키워드

참고문헌

  1. Bai, X.D., Cheng, W.C., Ong, D.E.L. and Ge Li. (2021), "Evaluation of geological conditions and clogging of tunneling using machine learning", Geomech. Eng., 25(1), 59-73. https://doi.org/10.12989/gae.2021.25.1.059.
  2. Choobbasti, A., Farrokhzad, F. and Barari, A. (2009), "Prediction of slope stability using artificial neural network (case study: Noabad, Mazandaran, Iran)", Arabian J. Geosci., 2(4), 311-319. https://doi.org/10.1007/S12517-009-0035-3.
  3. Das, S.K., Biswal, R.K., Sivakugan, N. and 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.
  4. Erzin, Y. and 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.
  5. Feng, X. (2000), "Introduction of intelligent rock mechanics", Science Press, Beijing, China.
  6. Feng, X., Li, S., Yuan, C., Zeng, P. and Sun Y. (2018), "Prediction of slope stability using naive bayes classifier", KSCE J. Civil Eng., 22, 941-950. https://doi.org/10.1007/s12205-018-1337-3.
  7. Glowacz, A. (2021a), "Fault diagnosis of electric impact drills using thermal imaging", Measurement, 171, 108815. https://doi.org/10.1016/j.measurement.2020.108815.
  8. Glowacz, A. (2021b), "Ventilation diagnosis of angle grinder using thermal imaging", Sensors, 21(8), 2853. https://doi.org/10.3390/s21082853.
  9. Gordan, B., Jahed Armaghani, D., Hajihassani, M. and 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.
  10. Hoang, N.D. and Pham, A.D. (2016), "Hybrid artificial intelligence approach based on metaheuristic and machine learning for slope stability assessment: A multinational data analysis", Exp. Syst. Appl., 46, 60-68. https://doi.org/10.1016/j.eswa.2015.10.020.
  11. Huang, H., Guo, M., Zhang, W. and Huang, M. (2022), "Seismic behavior of strengthened RC columns under combined loadings", J. Bridge Eng., 27(6). https://doi.org/10.1061/(ASCE)BE.1943-5592.0001871.
  12. Huang, Z., Cui, J. and Liu, H. (2004), "Chaotic neural network method for slope stability prediction", Chinese J. Rock Mech. Eng., 23(22). http://www.rockmech.org/EN/Y2004/V23/I22/3808.
  13. Khajehzadeh, M., Taha, M.R., El-Shafie, A. and Eslami, M. (2012), "A modified gravitational search algorithm for slope stability analysis", Eng. Appl. Artif. Intell., 25(8), 1589-1597. https://doi.org/10.1016/j.engappai.2012.01.011.
  14. Khishe, M. and Mosavi, M.R. (2019), "Improved whale trainer for sonar datasets classification using neural network", Appl. Acoust., 154, 176-192. https://doi.org/10.1016/j.apacoust.2019.05.006.
  15. Khishe, M. and Mosavi, M.R. (2020), "Chimp optimization algorithm", Exp. Syst. Appl., 149, 113338. https://doi.org/10.1016/j.eswa.2020.113338.
  16. Kolapo, P., Oniyide, G.O., Said, K.O., Lawal, A.I., Onifade, M. and Munemo, P. (2022), "An overview of slope failure in mining operations", Mining, 2(2), 350-384. https://doi.org/10.3390/mining2020019.
  17. Li, Q., Song, D., Yuan, C. and Nie, W. (2022), "An image recognition method for the deformation area of open-pit rock slopes under variable rainfall", Measurement, 188, 110544. https://doi.org/10.1016/j.measurement.2021.110544.
  18. Li, S., Zhao, H.B. Ru, Z. (2013), "Slope reliability analysis by updated support vector machine and Monte Carlo simulation", Nat. Hazards, 65(1), 707-722. https://doi.org/10.1007/s11069-012-0396-x.
  19. 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.
  20. Lin, Y., Zhou, K. and 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.
  21. Liu, J., Jiang, Y., Zhang, Y. and Sakaguchi, O. (2021a), "Influence of different combinations of measurement while drilling parameters by artificial neural network on estimation of tunnel support patterns", Geomech. Eng., 25(6), 439-454. https://doi.org/10.12989/gae.2021.25.6.439.
  22. Liu, L.L., Yang, C. and Wang, X.M. (2021b), "Landslide susceptibility assessment using feature selection-based machine learning models", Geomech. Eng., 25(1), 1-16. https://doi.org/10.12989/gae.2021.25.1.001.
  23. Liu, Z., Shao, J., Xu, W., Chen, H. and 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.
  24. Lu, P. and Rosenbaum, M.S. (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.
  25. Lu, S., Ban, Y., Zhang, X., Yang, B., Liu, S., Yin, L. and Zheng, W. (2022), "Adaptive control of time delay teleoperation system with uncertain dynamics", Front. Neurorobotics, 16. https://doi.org/10.3389/fnbot.2022.928863.
  26. Luat, N.V., Lee, K. and Duc-Kien, Thai. (2020), "Application of artificial neural networks in settlement prediction of shallow foundations on sandy soils", Geomech. Eng., 20(5), https://doi.org/10.12989/gae.2020.20.5.385.
  27. Mahmoodzadeh, A., Mohammadi, M., Abdulhamid, S., Ibrahim, H., Hama Ali, H. and Salim, S. (2021), "Dynamic reduction of time and cost uncertainties in tunneling projects", Tunn. Undergr. Sp. Tech., 109, 103774. https://doi.org/10.1016/j.tust.2020.103774.
  28. Mahmoodzadeh, A., Mohammadi, M., Salim, S., Hama Ali, H., Ibrahim, H., Abdulhamid, S., Nejati, H.R. and Rashidi, S. (2022a), "Machine learning techniques to predict rock strength parameters", Rock Mech. Rock Eng., 55, 1721-1741. https://doi.org/10.1007/s00603-021-02747-x.
  29. Mahmoodzadeh, A., Nejati, H.R., Mohammadi, M., Ibrahim, H., Khishe, M., Rashidi, S. and Hama Ali, H. (2022b), "Prediction of Mode-I rock fracture toughness using support vector regression with metaheuristic optimization algorithms", Eng. Fract. Mech., 264, 108334. https://doi.org/10.1016/j.engfracmech.2022.108334.
  30. Mahmoodzadeh, A., Rashidi, S., Mohammed, A., Hama Ali, H. and Ibrahim, H. (2022), "Machine learning approaches to enable resource forecasting process of road tunnels construction", Communication Engineering and Computer Science, North America, mar. 2022. https://conferences.cihanuniversity.edu.iq/index.php/COCOS/22/paper/view/718.
  31. Rukhaiyar, S., Alam, M. and 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.
  32. Samui, P. (2008), "Slope stability analysis: A support vector machine approach", Environ. Geology, 56(2), 255-267. https://doi.org/10.1007/s00254-007-1161-4.
  33. Suman, S., Khan, S., Das, S. and Chand, S. (2016), "Slope stability analysis using artificial intelligence techniques", Nat. Hazards, 84(2), 727-748. https://doi.org/10.1007/s11069-016-2454-2.
  34. Suman, S., Khan, S.Z., Das, S.K. and Chand SK. (2016), "Slope stability analysis using artificial intelligence techniques", Nat. Hazards, 84, 727-748. https://doi.org/10.1007/s11069-016-2454-2.
  35. Verma, A., Singh, T., Chauhan, N.K. and Sarkar, K. (2016), "A hybrid FEM-ANN approach for slope instability prediction", J. Inst. Engineers (India): Series A, 97(3), 171-180. https://doi.org/10.1007/s40030-016-0168-9.
  36. Wang, G., Zhao, B., Wu, B., Zhang, C. and Liu, W. (2022a), "Intelligent prediction of slope stability based on visual exploratory data analysis of 77 in situ cases", Int. J. Min. Sci. Tech., https://doi.org/10.1016/j.ijmst.2022.07.002
  37. Wang, J., Tian, J., Zhang, X., Yang, B., Liu, S., Yin, L. and Zheng, W. (2022b), "Control of time delay force feedback teleoperation system with finite time convergence", Front. Neurorobotics, 16. https://doi.org/10.3389/fnbot.2022.877069.
  38. Xi, Y., Jiang, W., Wei, K., Hong, T., Cheng, T. and Gong, S. (2022), "Wideband RCS reduction of microstrip antenna array using coding metasurface with low Q resonators and fast optimization method", IEEE Antennas Wireless Propagation Lett., 21(4), 656-660. https://doi.org/10.1109/LAWP.2021.3138241.
  39. Xiang, G., Yin, D., Cao, C. and Yuan, L. (2021), "Application of artificial neural network for prediction of flow ability of soft soil subjected to vibrations", Geomech. Eng., 25(5), 395-403. https://doi.org/10.12989/gae.2021.25.5.395.
  40. Xie, W., Li, X., Jian, W., Yang, Y., Liu, H., Robledo, L.F. and Nie, W. (2021a), "A novel hybrid method for landslide susceptibility mapping-based geodetector and machine learning cluster: A case of Xiaojin county, China", ISPRS Int. J. Geo-Inform., 10(2), 93. https://doi.org/10.3390/ijgi10020093.
  41. Xie, W., Nie, W., Saffari, P., Robledo, L.F., Descote, P.Y. and Jian, W. (2021b), "Landslide hazard assessment based on Bayesian optimization-support vector machine in Nanping City, China", Nat. Hazards, 109(1), 931-948. https://doi.org/10.1007/s11069-021-04862-y.
  42. Xu, J., Park, S.H., Zhang, X. and Hu, J. (2022a), "The improvement of road driving safety guided by visual inattentional blindness", IEEE T. Intell. Transp. Syst., 23(6), 4972-4981. https://doi.org/10.1109/TITS.2020.3044927.
  43. Xu, J., Zhou, L., Li, Y., Ding, J., Wang, S. and Cheng, W.C. (2022b), "Experimental study on uniaxial compression behavior of fissured loess before and after vibration", Int. J. Geomech., 22(2). https://doi.org/10.1061/(ASCE)GM.1943-5622.0002259.
  44. Xue, X. (2017), "Prediction of slope stability based on hybrid PSO and LSSVM", J. Comput. Civil Eng., 31(1). https://doi.org/10.1061/(ASCE)CP.1943-5487.0000607.
  45. Xue, X., Yang, X. and 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.
  46. Zhang, C. and Abedini, M. (2021), "Time-history blast response and failure mechanism of RC columns using Lagrangian formulation", Structures, 34, 3087-3098. https://doi.org/10.1016/j.istruc.2021.09.073.
  47. Zhao, H., Yin, S. and Ru, Z. (2012), "Relevance vector machine applied to slope stability analysis", Int. J. Numer. Anal. Method. Geomech., 36(5), 643-652. https://doi.org/10.1002/nag.1037.
  48. Zhao, H.B. (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.
  49. Zhou, J., Li, E., Yang, S., Wang, M., Shi, X., Yao, S. and Mitri, H.S. (2019), "Slope stability prediction for circular mode failure using gradient boosting machine approach based on an updated database of case histories", Safety Sci., 118, 505-518. https://doi.org/10.1016/j.ssci.2019.05.046.
  50. Zhou, J., Li, X. and Mitri, H.S. (2016), "Classification of rockburst in underground projects: Comparison of ten supervised learning methods", J. Comput. Civil Eng., 30(5). https://doi.org/10.1061/(ASCE)CP.1943-5487.0000553.