Prediction of S-wave arrival point for bender element tests using deep learning techniques with high-dimensional features in frequency domain

Abstract

The bender element (BE) method is a low-cost, nondestructive method for determining the shear modulus of soils. Therefore, methods have been proposed to determine the arrival point of S-waves, which is often difficult in the BE method, but there is no general-purpose method for all types of soils and test conditions. In this report, a deep learning model using high-dimensional features in the frequency domain was developed and validated for general-purpose and highly accurate S-wave arrival point prediction. The model was trained using 4/5 of tens of thousands of artificial waveforms, and a model with high validation accuracy was created for the remaining 1/5 of the artificial waveforms. The best prediction error for the 173 experimental data used to test the model was 11.88%, indicating a certain level of significance of the high-dimen-sional features. On the other hand, the prediction accuracy was lower than that of Momiama and Ogino’s previous model, again demonstrating the usefulness of the low-dimensional features.