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Least square support vector machine and relevance vector machine for evaluating seismic liquefaction potential using SPT

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Abstract

The determination of liquefaction potential of soil is an imperative task in earthquake geotechnical engineering. The current research aims at proposing least square support vector machine (LSSVM) and relevance vector machine (RVM) as novel classification techniques for the determination of liquefaction potential of soil from actual standard penetration test (SPT) data. The LSSVM is a statistical learning method that has a self-contained basis of statistical learning theory and excellent learning performance. RVM is based on a Bayesian formulation. It can generalize well and provide inferences at low computational cost. Both models give probabilistic output. A comparative study has been also done between developed two models and artificial neural network model. The study shows that RVM is the best model for the prediction of liquefaction potential of soil is based on SPT data.

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References

  • Christian JT, Swiger WF (1975) Statistics of liquefaction and SPT results. J Geotech Eng 101(11):1135–1150

    Google Scholar 

  • Ferreira LV, Kaszkurewicz E, Bhaya A (2005) Solving systems of linear equations via gradient systems with discontinuous righthand sides: application to LS-SVM. IEEE Trans Neural Netw 16(2):501–505

    Google Scholar 

  • Fletcher R (1987) Practical methods of optimization. Wiley, Chichester, New York

    Google Scholar 

  • Goh ATC (1994) Seismic liquefaction potential assessed by neural networks. J Geotech Eng 120(9):1467–1480

    Article  Google Scholar 

  • Haldar A, Tang WH (1979) Probabilistic evaluation of liquefaction potential. J Geotech Eng 104(2):145–162

    Google Scholar 

  • Kecman V (2001) Learning and soft computing: support vector machines, neural networks, and fuzzy logic models., The MIT press, Cambridge, Massachusetts, London, England

    Google Scholar 

  • Lai SY, Chang WJ, Ping-Sien Lin PS (2006) Logistic regression model for evaluating soil liquefaction probability using CPT data. J Geotech Geoenviron Eng 132(6):694–704

    Google Scholar 

  • Liao SSC, Veneziano D, Whitman RV (1988) Regression models for evaluating liquefaction probability. J Geotech Eng 114(4):389–411

    Article  Google Scholar 

  • Lu C, Gestel TV, Suykens JAK, Huffel SV, Vergote I, Timmerman D (2003) Preoperative prediction of malignancy of ovarian tumors using least squares support vector machines. Artif Intell Med 28(3):281–306

    Article  Google Scholar 

  • MacKay DJ (1994) Bayesian methods for backpropagation networks, in models of neural networks III. Springer, New York

  • MathWork Inc (1999) Matlab user’s manual. Version 5.3. The MathWorks Inc, Natick, MA

  • Neal R (1994) Bayesian learning for neural networks. Ph.D thesis, University of Toronto, Toronto, Quebe, Canada

  • Park D, Rilett LR (1999) Forecasting freeway link ravel times with a multi-layer feed forward neural network. Comput Aided Civil Infra Struct Eng 14:358–367

    Google Scholar 

  • Samui P (2007) Seismic liquefaction potential assessed by relevance vector machine. J Earthquake Eng Eng Vib 6(4):331–336

    Article  Google Scholar 

  • Seed HB, Idriss IM (1967) Analysis of soil liquefaction: Niigata earthquake. J Soil Mech Found Div ASCE 93(3):83–108

    Google Scholar 

  • Seed HB, Idriss IM (1971) Simplified procedure for evaluating soil liquefaction potential. J Soil Mech Found Div ASCE 97(9):1249–1273

    Google Scholar 

  • Seed HB, Idriss IM, Arango I (1983) Evaluation of liquefaction potential using field performance data. J Geotech Eng Div ASCE 109(3):458–482

    Google Scholar 

  • Seed HB, Tokimatsu K, Harder LF, Chung RM (1984) Influence of SPT procedures in soil liquefaction resistance evaluation. Rep. No. UCB/EERC-84/15, Earthquake Engineering Research Center, University of California, Berkeley, Calif

  • Sincero AP (2003) Predicting mixing power using artificial neural network. EWRI World Water and Environmental

  • Suykens JAK, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(3):293–300

    Google Scholar 

  • Suykens JAK, Lukas L, Dooren PV, Moor BD, Vandewalle J (1999) Least squares support vector machine classifiers: a large scale algorithm. In Proceedings of the European conference on circuit theory and design (ECCTD’99), Stresa, Italy, pp 839–842

  • Suykens JAK, Gestel TV, Brabanter JD, Moor BD, Vandewalle J (2002) Least squares support vector machines. World Scientific Publishing, Singapore

  • Tipping ME (2000) The relevance vector machine. Adv Neural Inf Process Syst 12:625–658

    Google Scholar 

  • Tipping ME (2001) Sparse Bayesian learning and the relevance vector machine. J Mach Learn 1:211–244

    Google Scholar 

Download references

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Authors and Affiliations

  1. Centre for Disaster Mitigation and Management, VIT University, Vellore, 632014, India

    Pijush Samui

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  1. Pijush Samui
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Correspondence to Pijush Samui.

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Samui, P. Least square support vector machine and relevance vector machine for evaluating seismic liquefaction potential using SPT. Nat Hazards 59, 811–822 (2011). https://doi.org/10.1007/s11069-011-9797-5

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  • DOI: https://doi.org/10.1007/s11069-011-9797-5

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