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. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

中文翻译：

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用于桥梁结构损伤诊断的多通道域自适应深度迁移学习

深度学习在桥梁损伤诊断中的成功应用依赖于训练和测试数据集服从相同分布的假设。然而，桥梁在使用中的损坏状态标签数据很难获得。否则，由于来自不同工作环境或桥梁的数据分布差异，很难应用使用桥梁 A（源域）训练的模型来诊断桥梁 B（目标域）。针对这些问题，在迁移学习的推动下，本文提出了一种新的桥梁损伤诊断方法，即基于多通道域自适应深度迁移学习的方法（MDADTL）。首先，引入基于 CNN 的多通道多尺度特征提取器来提取特征。第二，提出了一种基于最大均值差异（MMD）的多通道域自适应模块用于迁移学习，使得学习到的特征是域不变的。通过上述过程，用实验室或测试桥梁获得的标记数据训练的 MDADTL 有望用未标记数据诊断其他桥梁。实验证明了所提方法的有效性和先进性。这一探索将促进深度学习在桥梁损伤诊断中的实际应用。© 2022 日本电气工程师学会。由 Wiley Periodicals LLC 出版。实验证明了所提方法的有效性和先进性。这一探索将促进深度学习在桥梁损伤诊断中的实际应用。© 2022 日本电气工程师学会。由 Wiley Periodicals LLC 出版。实验证明了所提方法的有效性和先进性。这一探索将促进深度学习在桥梁损伤诊断中的实际应用。© 2022 日本电气工程师学会。由 Wiley Periodicals LLC 出版。