SPT-based liquefaction potential assessment by relevance vector machine approach

Abstract

This article adopts Relevance Vector Machine (RVM) for the determination of the liquefaction potential of soil based on Standard Penetration Test (SPT) data from Chi-Chi earthquake. RVM is a probabilistic sparse kernel model. This study uses RVM for solving binary classification problem. Two models (MODEL I and MODEL II) have been developed. Cyclic Stress Ratio and SPT blow count (N) have been used as input variables for MODEL I. MODEL II uses Peak Ground Acceleration (PGA) and N as input variables. The developed RVM model gives equations for the prediction of the liquefaction potential of soil. A comparative study has been presented between the developed RVM and the Artificial Neural Network (ANN). The results confirm that the developed RVM is a robust model for the prediction of the liquefaction potential of soil.

Keywords:

• relevance vector machine
• liquefaction
• prediction
• likehood
• artificial neural network