Least square support vector machine and relevance vector machine for evaluating seismic liquefaction potential using SPT |
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| Authors: | Pijush Samui |
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| Institution: | (1) Centre for Disaster Mitigation and Management, VIT University, Vellore, 632014, India |
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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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