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Damage assessment in beam-like structures by correlation of spectrum using machine learning

Autor(en): ORCID



Medium: Fachartikel
Sprache(n): Englisch
Veröffentlicht in: Frattura ed Integrità Strutturale, , n. 65, v. 17
Seite(n): 300-319
DOI: 10.3221/igf-esis.65.20
Abstrakt:

Damage assessment in the actual operating process of the structure is a modern and exciting problem of construction engineering due to several practical knowledge about the current condition of the inspected structures. However, the problem faced is the difficulty in controlling the excitation in structures. Therefore, the output-based structural damage identification method is becoming attractive because of its potential to be applied to an actual application without being constrained by the collection of the information excitation source. An approach of damage assessment based on supervised Machine Learning is introduced in this study by using the correlation of spectral signal as an input feature for artificial neural network (ANN) and decision tree. The output of machine learning algorithms consists of the appearance of new cuts, the level of cutting and the cutting position. A supported beam model was constructed as an experiment to determine if the method is reasonable for engineering structures. Two machine learning algorithms have been applied to check the relevance of the proposed feature from vibration data. This study contributes a standard in the damage identification problem based on spectral correlation.

Structurae kann Ihnen derzeit diese Veröffentlichung nicht im Volltext zur Verfügung stellen. Der Volltext ist beim Verlag erhältlich über die DOI: 10.3221/igf-esis.65.20.
  • Über diese
    Datenseite
  • Reference-ID
    10739826
  • Veröffentlicht am:
    01.09.2023
  • Geändert am:
    01.09.2023
 
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