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Seismic liquefaction potential assessment by using relevance vector machine

Pijush Samui

Department of Civil Engineering, Indian Institute of Science, Bangalore 560012, India

Abstract: Determining the liquefaction potential of soil is important in earthquake engineering. This study proposes the use of the Relevance Vector Machine (RVM) to determine the liquefaction potential of soil by using actual cone penetration test (CPT) data. RVM is based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. The results are compared with a widely used artificial neural network (ANN) model. Overall, the RVM shows good performance and is proven to be more accurate than the ANN model. It also provides probabilistic output. The model provides a viable tool for earthquake engineers to assess seismic conditions for sites that are susceptible to liquefaction.

Keywords: liquefaction; cone penetration test; relevance vector machine; artificial neural network