A fundamental study on transfer learning for damage evaluation of RC structures using hitting sound data

Abstract

This research aims to enhance the damage detection accuracy of RC structures using hitting sound data under conditions of limited training data. Unlike conventional approaches that focus on increasing the size of the training dataset, this study proposes a novel transfer learning methodology that effectively utilizes selected data from the test dataset. In a damage identification method based on Local Outlier Factor (LOF), high-precision damage detection is achieved even with a limited dataset by incorporating data points with small LOF values from the test data of a specific RC specimen into the training data of another RC specimen with the same specification. This demonstrates the potential for application in practical damage detection systems.