A STUDY ON QUANTIFICATION OF IMPACT-ECHO MONITORING WITH MACHINE LEARNING

Abstract

Impact-Echo monitoring is widely used as a method to find the flaking or cracking caused by deterioration of concrete structures. However, the accuracy of the monitoring depends on the skill and experience of the engineer. In this paper, we investigated the possibility of machine learning to quantify the Impact-Echo monitoring. Specifically, the impact sounds were imaged by spectrogram conversion and classified by a convolutional neural network. This method was tested on concrete test specimens with simulated defects and also salt-damaged real structures. It was possible to diagnose concrete defects with the same accuracy as a skilled engineer by using small image data. It was shown that the obtained neural network has a certain generalization performance.