Multi‐Channel Domain Adaptation Deep Transfer Learning for Bridge Structure Damage Diagnosis

Abstract

The successful application of deep learning in bridge damage diagnosis relies on the assumption that the training and test data sets obey the same distribution. However, it is difficult to obtain labeled data of damage status for a bridge in using. Otherwise, it is difficult to apply a model trained with bridge A (source domain) to diagnose bridge B (target domain) because of the distribution discrepancy of data from different working environments or bridges. In response to these problems, motivated by transfer learning, a new bridge damage diagnosis method, namely, the multichannel domain adaptation deep transfer learning based method (MDADTL), is proposed in this paper. First, a CNN based multichannel multi-scale feature extractor is introduced to extract features. Second, a multichannel domain adaptation module based on maximum mean discrepancy (MMD) is proposed for transfer learning, so that the learned features are domain-invariant. Through the above process, MDADTL trained with labeled data obtained in the laboratory or the testing bridge is expected to diagnose other bridges with unlabeled data. Experiments prove the effectiveness and advancement of the proposed method. This exploration will promote the practical application of deep learning in bridge damage diagnosis.

GMAW welding procedure expert system based on machine learning

“…As for the knowledge base design, Zhang [23] uses the traditional design pattern and divides the design into knowledge representation and knowledge acquisition. Liu et al use knowledge graph technology to build an expert system [24] . The system crawls the data on the Internet, performs format conversion, entity recognition and other processing on the data, which accumulates frequently asked questions and their solutions to form the knowledge base, and provides a new idea for the knowledge acquisition of the expert system.…”