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B-CNN: a deep learning method for accelerometer-based fatigue cracks monitoring system

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Abstract

The maintenance of fatigue damage is essential to keep steel bridges safe since the fatigue crack can lead to brittle fracture. However, real-time monitoring of fatigue crack propagation using traditional strain-based sensors is still a critical challenge due to their limited ductility and durability. In this paper, we propose a fatigue crack monitoring system by employing accelerometers due to their portability, easy replaceability, and sustainability. A corresponding blind source separation (BSS) joint convolutional neural network (CNN) classifier, namely B-CNN, is also proposed to identify fatigue crack conditions by interpreting acceleration measurements directly. We introduce the BSS as the first feature conversion layer to identify the pseudo modal properties from the highly sampled acceleration measurements before any further deep learning progress conducted by CNN. Those converted features are the sparse representation of the structural performance, where the crack conditions are hidden. By adopting BSS, the time history data can be sparse expressed, and the subsequent feature learning could be accelerated by employing a shallow CNN configuration. The proposed B-CNN classifier is further utilized for predicting crack conditions from forthcoming measurements. To examine the proposed monitoring solution, an experimental case study on a steel girder is conducted. About 1-month acceleration measurements were collected and investigated in this paper. The results obtained can demonstrate a shallow B-CNN configuration is sufficient to accurately identify cracks condition with high precision, over 93%, and proper prospection on unseen measurements.

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Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions. These data can be provided by the second author, if all partners agree with the provision. These data can only be used for research, and publishing results based on those data should be approved by the second author in advance.

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Acknowledgements

This study was carried out as cooperative research with the Tokyo Metropolitan Expressway Co., Ltd., Shutoko Engineering Co., Ltd., and the Highway Technology Research Center. The acceleration measurements were supported by Kyowa Electronic Instruments. This work is partial supported by the National Natural Science Foundation of China (Grant No. 52108118).

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Authors and Affiliations

  1. Department of Urban and Civil Engineering, Tokyo City University, 1-28-1 Tamazutsumi, Setagaya-ku, Tokyo, 158-8557, Japan

    Yanjie Zhu, Hidehiko Sekiya & Shogo Morichika

  2. Department of Bridge Engineering, School of Transportation, Southeast University, Nanjing, 211189, China

    Yanjie Zhu

  3. RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo, Tokyo, 103-0027, Japan

    Hidehiko Sekiya & Takayuki Okatani

  4. Graduate School of Information Sciences, Tohoku University, 6-3-09 Aoba, Aramaki-aza Aoba-ku, Sendai, 980-8579, Japan

    Takayuki Okatani

  5. Civil Engineering Program, School of Engineering, Faculty of Engineering, University of the Ryukyus, 1 Senbaru, Nishihara, Okinawa, 903-0213, Japan

    Masayuki Tai

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  1. Yanjie Zhu
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Correspondence to Yanjie Zhu.

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Zhu, Y., Sekiya, H., Okatani, T. et al. B-CNN: a deep learning method for accelerometer-based fatigue cracks monitoring system. J Civil Struct Health Monit 13, 947–959 (2023). https://doi.org/10.1007/s13349-023-00690-9

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  • DOI: https://doi.org/10.1007/s13349-023-00690-9

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