Study on the generalization of structural damage estimation using domain adaptation: experimental investigation with wooden specimens

Abstract

Recently, nondestructive evaluation techniques using deep learning have attracted attention, and structural damage inference methods employing convolutional neural networks (CNNs) have been proposed. However, classification accuracy tends to degrade when the distribution of test data differs from that of the training data. In this study, simulated damage conditions were reproduced by drilling holes in wooden specimens, and a CNN model was constructed to classify the damage into three levels. To develop a generalizable model capable of accurately classifying not only the same type of wood but also different types, a domain adaptation method using a Domain-Adversarial Neural Network (DANN) was introduced and its effectiveness was validated. The results showed that semi-supervised learning, which incorporates a portion of labeled target domain data into training, significantly improved classification accuracy.