A new deep transfer learning method for intelligent bridge damage diagnosis based on muti-channel sub-domain adaptation

Abstract

Deep learning has been successfully and widely used in the field of intelligent diagnosis to accurately identify damage. These successes are based on the assumption that the training and test data are independent and identically distributed. They ignore that there is a distribution discrepancy between training (source domain) and test data (target domain) obtained under different working environments, loads, or bridges. This distribution discrepancy leads to severe performance degradation of the diagnostic model. To address these problems, a novel intelligent diagnosis method is proposed for bridges, namely, the multi-channel subdomain adaptation based deep transfer learning network. In this network, a multi-scale-based parallel multi-channel feature extractor and a multi-kernel local maximum mean discrepancy-based multi-channel subdomain adaptation module are proposed to learn domain-invariant features. Data sets from three different bridges are used for transfer diagnosis experiments to verify the generality and superiority of the method. This study will further facilitate the intelligentisation of structural health monitoring systems in the field of bridges.