Geomechanics and Engineering

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Application of a support vector machine for prediction of piping and internal stability of soils

  • Xue, Xinhua (State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University)
  • Received : 2018.04.13
  • Accepted : 2019.07.18
  • Published : 2019.08.10
⟨ Previous Next ⟩

Abstract

Internal stability is an important safety issue for levees, embankments, and other earthen structures. Since a large part of the world's population lives near oceans, lakes and rivers, floods resulting from breaching of dams can lead to devastating disasters with tremendous loss of life and property, especially in densely populated areas. There are some main factors that affect the internal stability of dams, levees and other earthen structures, such as the erodibility of the soil, the water velocity inside the soil mass and the geometry of the earthen structure, etc. Thus, the mechanism of internal erosion and stability of soils is very complicated and it is vital to investigate the assessment methods of internal stability of soils in embankment dams and their foundations. This paper presents an improved support vector machine (SVM) model to predict the internal stability of soils. The grid search algorithm (GSA) is employed to find the optimal parameters of SVM firstly, and then the cross - validation (CV) method is employed to estimate the classification accuracy of the GSA-SVM model. Two examples of internal stability of soils are presented to validate the predictive capability of the proposed GSA-SVM model. In addition to verify the effectiveness of the proposed GSA-SVM model, the predictions from the proposed GSA-SVM model were compared with those from the traditional back propagation neural network (BPNN) model. The results showed that the proposed GSA-SVM model is a feasible and efficient tool for assessing the internal stability of soils with high accuracy.

Keywords

References

  1. Atallah, N., Shakoor, A. and Watts, C.F. (2015), "Investigating the potential and mechanism of soil piping causing water-level drops in Mountain Lake, Giles Country, Virginia", Eng. Geol., 195, 282-291. https://doi.org/10.1016/j.enggeo.2015.06.001.
  2. Balendra, M.M. and Ravi, S.S. (2017), "Assessment of slope stability using multiple regression analysis", Geomech. Eng., 13(2), 237-254. https://doi.org/10.12989/gae.2017.13.2.237.
  3. Castiglia, M., de Magistris, F.S. and Napolitano, A. (2018), "Stability of onshore pipelines in liquefied soils: Overview of computational methods", Geomech. Eng., 14(4), 355-366. http://doi.org/10.12989/gae.2018.14.4.355.
  4. Chang, D.S. and Zhang, L.M. (2013). "Extended internal stability criteria for soils under seepage." Soils Found., 53(4), 569-583. https://doi.org/10.1016/j.sandf.2013.06.008.
  5. Cristianini, N., Kandola, J., Elisseeff, A. and Shawe-Taylor, J. (2006), On Kernel Target Alignment, in Innovations in Machine Learning: Theory and Applications, Springer, 205-256.
  6. Dibike, Y.B., Velickov, S., Solomatine, D.P. and Abbott, M. (2001), "Model induction with support vector machines: Introduction and applications", J. Comput. Civ. Eng., 15(3), 208. https://doi.org/10.1061/(ASCE)0887-3801(2001)15:3(208).
  7. Fell, R., MacGregor, P., Stapledon, D. and Bell, G. (2005), Geotechnical Engineering of Dams, Balkema, Leiden, The Netherlands.
  8. Foster, M. and Fell, R. (1999), "A framework for estimating the probability of failure of embankment dams by internal erosion and piping using event tree methods", UNICIV R-377, University of New South Wales, Australia.
  9. Foster, M., Fell, R. and Spannagle, M. (2000), "The statistics of embankment dam failures and accidents", Can. Geotech. J., 37(5), 1000-1024. https://doi.org/10.1139/t00-030.
  10. Garner, S.J. and Fannin, R.J. (2010), "Understanding internal erosion: a decade of research following a sinkhole event", Int. J. Hydropower Dams, 17(3), 93-98.
  11. Huang, C.L. and Dun, J.F. (2008), "A distributed PSO-SVM hybrid system with feature selection and parameter optimization", Appl. Soft Comput., 8(4), 1381-1391. https://doi.org/10.1016/j.asoc.2007.10.007.
  12. Huang, C.L. and Wang, C.J. (2006), "A GA-based feature selection and parameters optimization for support vector machines", Expert Syst. Appl., 31(2), 231-240. https://doi.org/10.1016/j.eswa.2005.09.024.
  13. Kaveh, A., Hamze-Ziabari, S.M. and Bakhshpoori, T. (2018), "Soft computing-based slope stability assessment: A comparative study", Geomech. Eng., 14(3), 257-269. http://doi.org/10.12989/gae.2018.14.3.257.
  14. Lau, K.W. and Wu, Q.H. (2008), "Local prediction of non-linear time series using support vector regression", Pattern Recogn., 41(5), 1539-1547. https://doi.org/10.1016/j.patcog.2007.08.013.
  15. Lee, C.Y. and Chern S.G. (2013), "Application of a support vector machine for liquefaction assessment", J. Mar. Sci. Technol., 21(3), 318-324. https://doi.org/10.6119/JMST-012-0518-3.
  16. Lee, Y. and Lee, C. (2003), "Classification of multiple cancer types by multicategory support vector machines using gene expression data", Bioinformatics, 19, 1132-1139. https://doi.org/10.1093/bioinformatics/btg102.
  17. Maalouf, M., Khoury, N. and Trafalis, T.B. (2008), "Support vector regression to predict asphalt mix performance", Int. J. Numer. Anal. Meth. Geomech., 32(16), 1989-1996. https://doi.org/10.1002/nag.718.
  18. Osowski, S. and Garanty, K. (2007), "Forecasting of the daily meteorological pollution using wavelets and support vector machine", Eng. Appl. Artif. Intel., 20(6), 745-755. https://doi.org/10.1016/j.engappai.2006.10.008.
  19. Pham-Van, S., Hinkelmann, R., Nehrig, M. and Martinez, I. (2011), "A comparison of model concepts and experiments for seepage processes through a dike with a fault zone", Eng. Appl. Comput. Fluid Mech., 5(1), 149-158. https://doi.org/10.1080/19942060.2011.11015359.
  20. Rao, S.S. (2009), Engineering Optimization: Theory and Practice, John Wiley & Sons, Inc., Hoboken, New Jersey, U.S.A.
  21. Shin, K.S., Lee, T.S. and Kim, H.J. (2005), "An application of support vector machines in bankruptcy prediction model", Expert Syst. Appl., 28(1), 127-135. https://doi.org/10.1016/j.eswa.2004.08.009.
  22. Suykens, J.A.K., Vandewalle, J. and de Moor, B. (2001), "Optimal control by least squares support vector machines", Neural Networks, 14(1), 23-35. https://doi.org/10.1016/S0893-6080(00)00077-0.
  23. Vladimir, N. and Vapnik, V. (2000), The Nature of Statistical Learning Theory, Springer Verlag, New York, U.S.A.
  24. Wang, T.Y. and Chiang, H.M. (2007), "Fuzzy support vector machine for multi-class text categorization", Inform. Process. Manage., 43(4), 914-929. https://doi.org/10.1016/j.ipm.2006.09.011.
  25. Wei, G. and He, T.Y. (2017), "Displacement prediction in geotechnical engineering based on evolutionary neural network", Geomech. Eng., 13(5), 845-860. http://doi.org/10.12989/gae.2017.13.5.845.
  26. Xu, J.C., Ren, Q.W. and Shen, Z.Z. (2017), "Sensitivity analysis of the influencing factors of slope stability based on LS-SVM", Geomech. Eng., 13(3), 447-458. http://doi.org/10.12989/gae.2017.13.4.447.
  27. Zhang, L.M., Xu, Y. and Jia, J.S. (2009), "Analysis of earth dam failures-A database approach", Georisk, 3(3), 184-189. https://doi.org/10.1080/17499510902831759.
  28. Zhang, W.G. and Goh, A.T.C. (2016), "Evaluating seismic liquefaction potential using multivariate adaptive regression spines and logistic regression", Geomech. Eng., 10(3), 269-284. http://doi.org/10.12989/gae.2016.10.3.269.
  29. Zhang, W.H., Yu, G.S. and Cai, Y.Q. (2004), "Mechanism model and artificial intelligence method for prediction and judgment of piping occurring in embankment", J. Zhejiang Univ. Eng. Sci., 38(7), 902-908. (in Chinese).
  30. Zhao, Z.X., Chen, J.S. and Chen L. (2008), "Application of BP neural network to assessment of noncohesive piping-typed soils", Chin. J. Geotech. Eng., 30(4), 536-540. (in Chinese). https://doi.org/10.3321/j.issn:1000-4548.2008.04.012

Cited by

  1. Seismic fragility analysis of high concrete faced rockfill dams based on plastic failure with support vector machine vol.144, 2019, https://doi.org/10.1016/j.soildyn.2021.106587