The monitoring of large-scale civil infrastructure, such as railways and tunnels, using distributed sensing systems, for example, fiber-optic sensing, is cost-effective and hence highly promising. However, compared to conventional spot sensors, distributed sensing systems still face some challenges, for example, measurement accuracy and physical interpretability. In this study, we employ a distributed acoustic sensing (DAS) system to detect rail fastener failures and propose an interpretable cross-modal transfer learning approach to address these challenges. This method enables the fusion of acceleration and DAS signals within a deep learning framework, leveraging interpretability techniques to infer the rationale behind model predictions. Additionally, a field test focusing on rail fastener failures was conducted, during which the dynamic responses of the rail under various fastener failure conditions were independently measured using accelerometers and DAS. The proposed method was applied and validated using field test data. Results demonstrate that the deep learning model trained with the proposed cross-modal transfer learning method achieves superior accuracy compared to traditional methods using DAS data alone. Furthermore, interpretable features were successfully extracted in the proposed model for follow-up predictions, underscoring its enhanced generalization capability and interpretability for rail track monitoring. These results highlight not only the method’s effectiveness for rail monitoring but also its potential applicability to other civil structures requiring integrated distributed and point sensing.
Liang et al. (Sun,) studied this question.
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