DOI QR코드

DOI QR Code

A new model approach to predict the unloading rock slope displacement behavior based on monitoring data

  • Jiang, Ting (State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University) ;
  • Shen, Zhenzhong (State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University) ;
  • Yang, Meng (State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University) ;
  • Xu, Liqun (State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University) ;
  • Gan, Lei (State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University) ;
  • Cui, Xinbo (Information Center of Land and Resources)
  • 투고 : 2018.05.09
  • 심사 : 2018.06.21
  • 발행 : 2018.07.25

초록

To improve the prediction accuracy of the strong-unloading rock slope performance and obtain the range of variation in the slope displacement, a new displacement time-series prediction model is proposed, called the fuzzy information granulation (FIG)-genetic algorithm (GA)-back propagation neural network (BPNN) model. Initially, a displacement time series is selected as the training samples of the prediction model on the basis of an analysis of the causes of the change in the slope behavior. Then, FIG is executed to partition the series and obtain the characteristic parameters of every partition. Furthermore, the later characteristic parameters are predicted by inputting the earlier characteristic parameters into the GA-BPNN model, where a GA is used to optimize the initial weights and thresholds of the BPNN; in the process, the numbers of input layer nodes, hidden layer nodes, and output layer nodes are determined by a trial method. Finally, the prediction model is evaluated by comparing the measured and predicted values. The model is applied to predict the displacement time series of a strong-unloading rock slope in a hydropower station. The engineering case shows that the FIG-GA-BPNN model can obtain more accurate predicted results and has high engineering application value.

키워드

과제정보

연구 과제 주관 기관 : National Natural Science Foundation of China

참고문헌

  1. Cao, J. (2002), "Neural network modeling and analytical modeling of slope stability", Ph.D. Dissertation, University of Oklahoma, U.S.A.
  2. Chen, S.S., Fu, Z.Z., Wei, K.M. and Han, H.Q. (2016), "Seismic responses of high concrete face rockfill dams: A case study", Water Sci. Eng., 9(3), 195-204. https://doi.org/10.1016/j.wse.2016.09.002
  3. Chok, Y.H., Jaksa, M.B. and Kaggwa, W.S. (2016), "Neural network prediction of the reliability of heterogeneous cohesive slopes", Int. J. Numer. Anal. Met., 40(11), 1556-1569. https://doi.org/10.1002/nag.2496
  4. Choobbasti, A.J., Farrokhzad, F. and 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
  5. Choobbasti, A.J., Farrokhzad, F. and 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
  6. Hu, W. and Cao, W. (2015). "Slope stability evaluation based on hybrid algorithm of particle swarm optimization and BP neural network", J. Rail Way Sci. Eng., 12(1), 66-71.
  7. Hugues, G., Claude, P. and Jean-Claude, V. (2014), "A suitable methodology for assessing impacts of successive rainfalls infiltration on road slope stability", Acta Geotech., 9(5), 753-770. https://doi.org/10.1007/s11440-013-0292-x
  8. Jackson, R., Gunn, D.A. and Long, D. (2004), "Predicting variability in the stability of slope sediments due to earthquake ground motion in the AFEN area of the western UK continental shelf", Mar. Geol., 213(1-4), 363-378. https://doi.org/10.1016/j.margeo.2004.10.014
  9. Jiang, S.H., Li, D.Q., Zhang, L.M. and Zhou, C.B. (2014), "Slope reliability analysis considering spatially variable shear strength parameters using a non-intrusive stochastic finite element method", Eng. Geol., 168, 120-128. https://doi.org/10.1016/j.enggeo.2013.11.006
  10. Johari, A. and Javadi, A.A. (2012), "Reliability assessment of infinite slope stability using the jointly distributed random variables method", Sci. Iran., 19(3), 423-429. https://doi.org/10.1016/j.scient.2012.04.006
  11. Kaunda, R.B., Chase, R.B., Kehew, A.E., Kaugars, K. and Selegean, J.P. (2010), "Neural network modeling applications in active slope stability problems", Environ. Earth. Sci., 60(7), 1545-1558. https://doi.org/10.1007/s12665-009-0290-3
  12. Li, W. and Liu, L. (2009), "Application of fuzzy neural network for predicting natural slope failure due to underground mining in mountainous areas", Fuzzy Syst. Math., 23(3), 170-174.
  13. Lim, K., Li, A.J. and Lyamin, A.V. (2015), "Three-dimensional slope stability assessment of two-layered undrained clay", Comput. Geotech., 70, 1-17. https://doi.org/10.1016/j.compgeo.2015.07.011
  14. Mendjel, D. and Messast, S. (2012), "Development of limit equilibrium method as optimization in slope stability analysis", Struct. Eng. Mech., 41(3), 339-348. https://doi.org/10.12989/sem.2012.41.3.339
  15. Moon, H. (2010), "The prediction of rock slope stability in Korea using artificial neural network", J. Kor. Soc. Min. Energy Res. Eng., 47(1), 31-44.
  16. Nieuwenhuis, J.D. (1991), "The lifetime of a landslide: Investigations in the French alps", A. A. Balkema, Rotterdam, 144.
  17. Ozer, M. (2011), "An application of fuzzy information granulation in the emerging area of online sports", Exp. Syst. Appl., 38(4), 4514-4521. https://doi.org/10.1016/j.eswa.2010.09.125
  18. Ramin, T.M. (2017), "Seismic response of soil-structure interaction using the support vector regression", Struct. Eng. Mech., 63(1), 115-124. https://doi.org/10.12989/SEM.2017.63.1.115
  19. Ren, C., Liang, Y. and Pang, G. (2014), "The empirical mode decomposition and genetic algorithm-wavelet neural network for slope deformation prediction research", J. Geomat. Sci. Technol., 31(96), 551-555.
  20. Shu, S. and Gong, W. (2016), "An artificial neural network-based response surface method for reliability analyses of c-phi slopes with spatially variable soil", Chin. Ocean Eng., 30(1), 113-122. https://doi.org/10.1007/s13344-016-0006-x
  21. Su, H., Yang, M. and Wen, Z. (2016), "An approach using multifactor combination to evaluate high rocky slope safety", Nat. Hazard. Earth Syst., 16(6), 1449-1463. https://doi.org/10.5194/nhess-16-1449-2016
  22. Suman, S., Khan, S.Z. and Das, S.K. (2016), "Slope stability analysis using artificial intelligence techniques", Nat. Hazard., 84(2), 727-748. https://doi.org/10.1007/s11069-016-2454-2
  23. Taha, M., Khajehzadeh, M. and El-Shafie, A. (2012), "Application of particle swarm optimization in evaluating the reliability of soil slope stability analysis", Sains Malays., 41(7), 847-854.
  24. Tsai, P., Ahmad, H.B. and Chen, C. (2017), "Structural system simulation and control via NN based fuzzy model", Struct. Eng. Mech., 56(3), 385-407. https://doi.org/10.12989/sem.2015.56.3.385
  25. Won, P. (2005), "Stavility evaluation of the cut slope using artificial neural network model", J. Kor. Soc. Civil Eng., 25(4C), 275-283.
  26. Yang, H., Xie, S., Secq, J. and Shao, J. (2017a), "Experimental study and modeling of hydromechanical behavior of concrete fracture", Water Sci. Eng., 10(2), 97-106. https://doi.org/10.1016/j.wse.2017.06.002
  27. Yang, M., Su, H. and Wen, Z. (2017b), "An approach of evaluation and mechanism study on the high and steep rock slope in water conservancy project", Comput. Concrete, 19(5), 527-535. https://doi.org/10.12989/cac.2017.19.5.527
  28. Yang, M., Su, H. and Yan, X. (2015), "Computation and analysis of high rocky slope safety in a water conservancy project", Discr. Dyn. Nat. Soc., 197579.
  29. Yu, F.S. and Pedrycz, W. (2009), "The design of fuzzy information granules: Tradeoffs between specificity and experimental evidence", Appl. Soft Comput., 9(1), 264-273. https://doi.org/10.1016/j.asoc.2007.10.026
  30. Yu, W., Cai, J. and An, F. (2013), "On slope displacement prediction model based on lmd-bp neural network", Comput. Appl. Softw., 30(9), 107-109.
  31. Zhang, Y., Xu, W., Shao, J., Zhao, H. and Wang, W. (2017), "Experimental investigation of creep behavior of clastic rock in Xiangjiaba hydropower project", Water Sci. Eng., 8(1), 55-62. https://doi.org/10.1016/j.wse.2015.01.005