Influence of Training Dataset on Multiclass Classification of Rebar Corrosion Using Impact Sound Based on Neural Network

Abstract

The purpose of this study is to examine the applicability of a multi-class classification method for rebar corrosion using impact sounds based on a neural network model. In addition, the Influence of the composition of the training dataset (the number of data, contamination of mislabeled data) and the number of intermediate layer nodes on classification results were investigated. As a result, it was confirmed that by using a neural network model with three output nodes, it is possible to classify rebar corrosion into three classes: corrosion level of 0% (no corrosion), corrosion rate 1% and corrosion level of 6%. Furthermore, it was found that in order to improve classification accuracy, it is desirable to collect as much training data as possible. On the other hand, in the case that the number of intermediate layer nodes was excessive or in the case that mislabeled data was contaminated into the training dataset, the tendency for classification accuracy to decrease was confirmed.