Influence of Complementary Labeled Data on Judgement Results for Loading History of RC Beams by Hammering Sounds Using Neural Network and Its Removal Method

Abstract

The purpose of this study is to investigate the influence of complementary labeled data in the training data set on the judgment results of the load histories of RC beam members by hammering sounds using a neural network model. In addition, the applicability of the method of removing complementary labeled data using the local outlier factor method was examined. As a result, it was confirmed that the true positive rate tended to decrease when complementary labeled data was included compared to when there was no complementary labeled data. It was also indicated that most complementary labeled data can be removed by using the local outlier factor method. Furthermore, it was confirmed that the true positive rate tended to recover to the same level as the case without complementary labeled data in the judgment using the training data set after removal using the local outlier factor method.