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Novelty Detection Using Sparse Auto-Encoders to Characterize Structural Vibration Responses

  • Research Article-Civil Engineering
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Abstract

Deep learning techniques have been increasingly popular for detecting structural novelties in recent years. The deep learning notion originates from the theory of neural networks, and it comprises several machine learning approaches that were primarily created to solve high-dimensional and nonlinear problems due to their great data mapping capabilities. Although the basic ideas of such algorithms were established in the 1960s, their use in damage detection situations is still relatively new. In so doing, the current study assesses the Sparse Auto-Encoder (SAE) deep learning method when applied to the characterization of structural anomalies. The fundamental concept is to employ the SAE to extract significant features from monitored signals and the well-known Support Vector Machine (SVM) to classify those features within the framework of a Structural Health Monitoring (SHM) program. The proposed method is evaluated using vibration data from a numerical beam model and a highway viaduct in Brazil. The results demonstrate that the SAE can extract relevant properties from dynamic data, making it valuable for SHM applications.

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Acknowledgements

The authors would like to thank CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, PROCAD “88881.068530/2014-0”), CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico—Grants “407297/2021-9-PQ” and “311576/2018-4-PQ”), FAPEMIG (Fundação de Amparo à Pesquisa—projects “PPM-00106-17” and “PPM-0001-18”), UFJF (Universidade Federal de Juiz de Fora) and UFPB (Universidade Federal da Paraíba) for the financial support. Furthermore, the help of C. Teodoro, G. Ferreira and M. Araújo in collecting data from the Várzea Nova viaduct is fully acknowledged.

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Authors and Affiliations

  1. Graduate Program in Computational Modeling, Federal University of Juiz de Fora, Juiz de Fora, Brazil

    Rafaelle Piazzaroli Finotti & Flávio de Souza Barbosa

  2. Graduate Program in Civil Engineering, Federal University of Juiz de Fora, Juiz de Fora, Brazil

    Flávio de Souza Barbosa & Alexandre Abrahão Cury

  3. Graduate Program in Civil and Environment Engineering, Federal University of Paraíba, João Pessoa, Brazil

    Roberto Leal Pimentel

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  1. Rafaelle Piazzaroli Finotti
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  2. Flávio de Souza Barbosa
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Correspondence to Flávio de Souza Barbosa.

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Finotti, R.P., Barbosa, F.d., Cury, A.A. et al. Novelty Detection Using Sparse Auto-Encoders to Characterize Structural Vibration Responses. Arab J Sci Eng 47, 13049–13062 (2022). https://doi.org/10.1007/s13369-022-06732-6

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