Study on model development of damage classification and condition assessment using periodic bridge inspection images, and evaluation of model accuracy through modification of training images

Abstract

To ensure the efficient maintenance of bridges constructed during the period of rapid economic growth, periodic bridge inspections are conducted, and the condition of each bridge is assessed. The results of these inspections are publicly available and are expected to be used as learning data when building AI models. This study developed a model for damage classification and condition assessment using periodic bridge inspection images by employing convolutional neural network (CNN). The accuracy of CNN-based model was evaluated using several combinations of training data, moreover, was examined by modifying the training images to focus more closely on damaged areas. The applicability of periodic bridge inspection images to damage classification and condition assessment model was evaluated through these accuracy verifications.