Comparison of prediction performance for S-wave arrival point in bender element test using various machine learning models

Abstract

In Bender Element tests, determining the arrival time of the S-wave is often challenging from received waveforms. To improve the accuracy of S-wave arrival time prediction using machine learning as decision support for experimenters, we created three machine learning models based on support vector regression, Gaussian process regression, and neural network. We compared their prediction accuracies. We obtained 7240 artificial received waveforms with true S-wave arrival times from the linear system theory and trained the models using 11-dimensional features reflected from waveforms and test condition. The prediction performance of three models were compared using the errors between predicted arrival times and the values determined by an expert. The comparison revealed characteristics of each model in prediction and that Gaussian process regression model demonstrated the closest approximation to the values determined by the expert.