Machine-learning-based prediction of structural defects using acceleration response waveforms from hammering test

Abstract

In this study, we employed machine learning to predict the presence or absence of defects in structures, using acceleration response waveforms from a hammering test. The loss function was defined by the binary cross-entropy error, and several numerical experiments were performed to predict defects by changing the acceleration response waveforms. These acceleration response waveforms were used as input data, enabling the supervised machine learning model to output the presence or absence of defects.