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An unsupervised cross-domain method for bridge damage detection based on multichannel symmetric dot pattern feature alignment
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AI
DOI：10.1111/mice.70117.png)
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Abstract
En 中文
A critical issue for data-driven and machine learning-based damage detection of engineering infrastructures is associated with unlabeled datasets and distribution shifts in cross-domains. To overcome this challenge, this study develops an unsupervised cross-domain method for bridge damage detection based on interclass alignment of time-frequency features extracted from multichannel sensor data. The computational framework was developed based on a deep subdomain adaptation network integrating digital and physical information. Initially, a multichannel symmetric dot pattern was utilized to transform the structural acceleration signals into a comprehensive image. Subsequently, a convolutional block attention module-enhanced ResNet34 (CBAM-ResNet34) was constructed to extract discriminative time-frequency features, where a local maximum mean discrepancy principle was introduced to perform class-conditional alignment across subdomains. Compared with traditional global domain alignment methods, the proposed approach focuses on aligning class-conditional distributions within subdomains to improve the generalization performance with unlabeled datasets. The proposed method was validated on both simulated and experimental datasets collected from a laboratory-scaled steel truss bridge. Furthermore, a case study on the Old ADA Bridge in Japan was presented to demonstrate the robustness and practical applicability of the proposed approach, serving as a benchmark against classic unsupervised methods. The results show that the proposed framework has a substantial improvement in source-to-target transfer recognition performance. Discussions were conducted on the application prospects of the proposed framework for more in-service infrastructures in complex conditions.
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C
IF:
9.1
Papers:
2.0K
Citations:
10.0K