Multi-Task Convolutional Neural Network for Bridge Damage Assessment

Abstract

Application of deep learning methods to bridge inspection has been studied in order to resolve shortage of engineers and increasing cost for periodic bridge inspection. To improve the efficiency in bridge inspec-tion, AI model should support various damage types and assessment criteria. This study aims to propose a multi-task learning method related with damage classification for high-accuracy and robust damage assessment model. Normal multi-task learning and multi-task learning with attention mechanism relating damage classification to damage assessment explicitly are validated through the comparison with single-task learning method using bridge inspection data in Nagasaki Prefecture.