Deep learning enhanced principal component analysis for structural health monitoring

Ana Fernandez-Navamuel*, Filipe Magalhães, Diego Zamora-Sánchez, Ángel J. Omella, David Garcia-Sanchez, David Pardo

*Autor correspondiente de este trabajo

    Producción científica: Contribución a una revistaArtículorevisión exhaustiva

    26 Citas (Scopus)

    Resumen

    This paper proposes a Deep Learning Enhanced Principal Component Analysis (PCA) approach for outlier detection to assess the structural condition of bridges. We employ partially explainable autoencoder architecture to replicate and enhance the data compression and reconstruction ability of PCA. The particularity of the method lies in the addition of residual connections to account for nonlinearities. We apply the proposed method to monitoring data obtained from two bridges under real operation conditions and compare the results before and after adding the residual connections. Results show that the addition of residual connections enhances the outlier detection ability of the network, allowing to detect lighter damages.

    Idioma originalInglés
    Páginas (desde-hasta)1710-1722
    Número de páginas13
    PublicaciónStructural Health Monitoring
    Volumen21
    N.º4
    DOI
    EstadoPublicada - jul 2022

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