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

Pijush Samui ()

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2011, vol. 59, issue 2, 822 pages

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. Copyright Springer Science+Business Media B.V. 2011

Keywords: Liquefaction; Relevance vector machine; SPT; Artificial neural network; Least square support vector machine (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s11069-011-9797-5

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