Geomechanics and Engineering

  • 제13권5호
  • /
  • Pages.845-860
  • /
  • 2017
  • /
  • 2005-307X(pISSN)
  • /
  • 2092-6219(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Displacement prediction in geotechnical engineering based on evolutionary neural network

  • Gao, Wei (Key Laboratory of Ministry of Education for Geomechanics and Embankment Engineering, College of Civil and Transportation Engineering, Hohai University) ;
  • He, T.Y. (Key Laboratory of Ministry of Education for Geomechanics and Embankment Engineering, College of Civil and Transportation Engineering, Hohai University)
  • 투고 : 2016.10.25
  • 심사 : 2017.05.24
  • 발행 : 2017.11.25
⟨ 이전 논문 다음 논문 ⟩

초록

It is very important to study displacement prediction in geotechnical engineering. Nowadays, the grey system method, time series analysis method and artificial neural network method are three main methods. Based on the brief introduction, the three methods are analyzed comprehensively. Their merits and demerits, applied ranges are revealed. To solve the shortcomings of the artificial neural network method, a new prediction method based on new evolutionary neural network is proposed. Finally, through two real engineering applications, the analysis of three main methods and the new evolutionary neural network method all have been verified. The results show that, the grey system method is a kind of exponential approximation to displacement sequence, and time series analysis is linear autoregression approximation, while artificial neural network is nonlinear autoregression approximation. Thus, the grey system method can suitably analyze the sequence, which has the exponential law, the time series method can suitably analyze the random sequence and the neural network method almostly can be applied in any sequences. Moreover, the prediction results of new evolutionary neural network method is the best, and its approximation sequence and the generalization prediction sequence are all coincided with the real displacement sequence well. Thus, the new evolutionary neural network method is an acceptable method to predict the measurement displacements of geotechnical engineering.

키워드

과제정보

연구 과제 주관 기관 : Central Universities

참고문헌

  1. Aktas, G. and Ozerdem, M.S. (2016), "Prediction of behavior of fresh concrete exposed to vibration using artificial neural networks and regression model", Struct. Eng. Mech., 60(4), 655-665. https://doi.org/10.12989/sem.2016.60.4.655
  2. Aleshin, Y. and Torgoev, I. (2013), Landslide Prediction Based on Neural Network Modelling, Springer-Verlag, Berlin, Germany.
  3. Angin, Z. (2016), "Geotechnical field investigation on giresun hazelnut licenced warehause and spot exchange", Geomech. Eng., 10(4), 547-563. https://doi.org/10.12989/gae.2016.10.4.547
  4. Bao, L.W., He, M. and Shen, P. (1995), "Argument on the shortcomings of BP-modal", Patt. Recogn. Artif. Intell., 8(1), 1-5.
  5. Bizjak, K.F. and Petkovsek, B. (2004), "Displacement analysis of tunnel support in soft rock around a shallow highway tunnel at Golovec", Eng. Geol., 75(1), 89-106. https://doi.org/10.1016/j.enggeo.2004.05.003
  6. Box, G.E.P. and Jenkins, G.M. (1989), Time Series Analysis: Forecasting and Control, Holder-Day, San Francisco, California, U.S.A.
  7. Bozzano, F., Cipriani, I., Mazzanti, P. and Prestininzi, A. (2014), "A field experiment for calibrating landslide time-of-failure prediction functions", J. Rock Mech. Min. Sci., 67(2), 69-77.
  8. Chen, C. and Huang, S.J. (2013), "The necessary and sufficient condition for GM(1,1) grey prediction model", Appl. Math. Comput., 219(11), 6152-6162. https://doi.org/10.1016/j.amc.2012.12.015
  9. Chen, H.Q. and Zeng, Z.G. (2013), "Deformation prediction of landslide based on improved backpropagation neural network", Cogn. Comput., 5(1), 56-62. https://doi.org/10.1007/s12559-012-9148-1
  10. Chen, H.Q., Zeng, Z.G. and Tang, H.M. (2015), "Landslide deformation prediction based on recurrent neural network", Neur. Proc. Lett., 41(2), 169-178. https://doi.org/10.1007/s11063-013-9318-5
  11. Chen, J.J., Zeng, Z.Z., Jiang, P. and Tang, H.M. (2015), "Deformation prediction of landslide based on functional network", Neurocomput., 149, 151-157. https://doi.org/10.1016/j.neucom.2013.10.044
  12. Churing, Y. (1995), Backpropagation, Theory, Architecture and Applications, Lawrence Erbaum Publishers, New York, U.S.A.
  13. Feng, X.T. and An, H.G. (2004), "Hybrid intelligent method optimization of a soft rock replacement scheme for a large cavern excavated in alternate hard and soft rock strata", J. Rock Mech. Min. Sci., 41(4), 655-667. https://doi.org/10.1016/j.ijrmms.2004.01.005
  14. Gao, W. (2004), "Fast immunized evolutionary programming", Proceedings of the 3rd International Conference on Machine Learning and Cybernetics, Shanghai, China, August.
  15. Gao, W. and Yin, Z.X. (2011), Modern Intelligent Bionics Algorithm and Its Applications, Science Press, Beijing, China.
  16. Haeri, H. and Sarfarazi, V. (2016), "The deformable multilaminate for predicting the elasto-plastic behavior of rocks", Comput. Concrete, 18(2), 201-214. https://doi.org/10.12989/cac.2016.18.2.201
  17. Honik, K. (1991), "Approximation capabilities of multilayer feedforward neural network", Neur. Netw., 4(2), 551-557.
  18. Huang, Z.Q., Jiang, T., Yue, Z.Q., Lee, C.F. and Wang, S.J. (2003), "Deformation of the central pier of the permanent shiplock, three gorges project, China: An analysis case study", J. Rock Mech. Min. Sci., 40(40), 877-892. https://doi.org/10.1016/S1365-1609(03)00061-3
  19. Jacek, Z.M. (1992), Introduction to Artificial Neural Systems, West Publishing Company, St. Paul, Minnesota, U.S.A.
  20. Kayacan, E., Ulutas, B. and Kaynak, O. (2010), "Grey system theory-based models in time series prediction", Expert Syst. Appl., 37(2), 1784-1789. https://doi.org/10.1016/j.eswa.2009.07.064
  21. Lai, J.X., Qiu, J.L., Feng, Z.H., Chen, J.X. and Fan, H.B. (2016), "Prediction of soil deformation in tunnelling using artificial neural networks", Comput. Intel. Neurosci., 33.
  22. Li, X.H., Zhao, Y., Jin, X.G., Lu, X.Y. and Wang, X.F. (2005), "Application of grey majorized model in tunnel surrounding rock displacement forecasting", Adv. Nat. Comput., 3611, 584-591.
  23. Lian, C., Zeng, Z.G., Yao, W. and Tang, H.M. (2014), "Ensemble of extreme learning machine for landslide displacement prediction based on time series analysis", Neur. Comput. Appl., 24(1), 99-107. https://doi.org/10.1007/s00521-013-1446-3
  24. Liu, J.G., Zhou, D.D. and Liu, K.W. (2015), "A mathematical model to recover missing monitoring data of foundation pit", Geomech. Eng., 9(3), 275-286. https://doi.org/10.12989/gae.2015.9.3.275
  25. Liu, Z.B., Xu, W.Y., Meng, Y.D. amd Chen, H.J. (2009), "Modification of GM (1,1) and its application in analysis of rock-slope deformation", Proceedings of the 2009 IEEE International Conference on Grey Systems and Intelligent Services, Nanjing, China, November.
  26. Liu, Z.B., Xu, W.Y. and Shao, J.F. (2012), "Gaussian process based approach for application on landslide displacement analysis and prediction", Comp. Model. Eng. Sci., 84(2), 99-122.
  27. Lu, P. and Rosenbaum, M.S. (2003), "Artificial neural networks and grey systems for the prediction of slope stability", Nat. Haz., 30(3), 383-398. https://doi.org/10.1023/B:NHAZ.0000007168.00673.27
  28. Luo, F.L. and Unbehauen, R. (1997), Applied Neural Networks for Signal Processing, Cambridge University Press, New York, U.S.A.
  29. Mazek, S.A. (2014), "Evaluation of surface displacement equation due to tunnelling in cohesionless soil", Geomech. Eng., 7(1), 55-73. https://doi.org/10.12989/gae.2014.7.1.055
  30. Qiao, D.L. and Zhao, M. (2011), "Deformation prediction based on time series analysis and grey system theory", Adv. Mater. Res., 368, 2147-2152.
  31. Wu, Q.D., Yan, B., Zhang, C., Wang, L., Ning, G.B. and Yu, B. (2014), "Displacement prediction of tunnel surrounding rock: A comparison of support vector machine and artificial neural network", Math. Probl. Eng., 2014, Article ID 351496(6).
  32. Wu, Y., Yang, S.Z. and Tao, J.H. (1988), "Analysis on grey system prediction and time series analysis prediction", J. Huazhong U. Sci. Technol., 16(3), 27-34.
  33. Zhang, W.G. and Goh, A.T.C. (2016), "Predictive models of ultimate and serviceability performances for underground twin caverns", Geomech. Eng., 10(2), 157-188.
  34. Zhu, C. and Hu, G. (2013), "Time series prediction of landslide displacement using SVM model: Application to Baishuihe landslide in three gorges reservoir area, China", App. Mech. Mater., 239, 1413-1420.
  35. Zhu, Z.D., Li, H.B., Shang, J.F., Wang, W. and Liu, J.H. (2010), "Research on the mining roadway displacement forecasting based on support vector machine theory", J. Coal Sci. Eng., 16(3), 235-239. https://doi.org/10.1007/s12404-010-0303-6

피인용 문헌

  1. Landslide prediction based on a combination intelligent method using the GM and ENN: two cases of landslides in the Three Gorges Reservoir, China vol.17, pp.1, 2017, https://doi.org/10.1007/s10346-019-01273-w
  2. Tunnel Back Analysis Based on Differential Evolution Using Stress and Displacement vol.2020, pp.None, 2017, https://doi.org/10.1155/2020/8156573
  3. Multiple data-driven approach for predicting landslide deformation vol.17, pp.3, 2017, https://doi.org/10.1007/s10346-019-01320-6
  4. Evaluation of geological conditions and clogging of tunneling using machine learning vol.25, pp.1, 2017, https://doi.org/10.12989/gae.2021.25.1.059
  5. Data-driven framework for predicting ground temperature during ground freezing of a silty deposit vol.26, pp.3, 2017, https://doi.org/10.12989/gae.2021.26.3.235