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Field experiment on a PSC-I bridge for convolutional autoencoder-based damage detection

Autor(en):



Medium: Fachartikel
Sprache(n): Englisch
Veröffentlicht in: Structural Health Monitoring, , n. 4, v. 20
Seite(n): 147592172092626
DOI: 10.1177/1475921720926267
Abstrakt:

In this study, a field experiment was performed for damage detection on a PSC-I bridge based on a convolutional autoencoder using the damage detection approach proposed in a previous study by the authors. The field experiment measured the acceleration and strain data of the PSC-I bridge while a single vehicle passed the bridge; subsequently, these data were used to train and test the convolutional autoencoder–based damage detection model. The results of the test showed that the convolutional autoencoder–based model could perform accurate and robust damage detection. Furthermore, these findings indicate that the convolutional autoencoder–based damage detection could also perform satisfactorily in practice. The results of this study can form the basis to facilitate the adoption of the convolutional autoencoder–based damage detection method to monitor bridges in practice.

Structurae kann Ihnen derzeit diese Veröffentlichung nicht im Volltext zur Verfügung stellen. Der Volltext ist beim Verlag erhältlich über die DOI: 10.1177/1475921720926267.
  • Über diese
    Datenseite
  • Reference-ID
    10562442
  • Veröffentlicht am:
    11.02.2021
  • Geändert am:
    09.07.2021
 
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