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Correlation coefficients of vibration signals and machine learning algorithm for structural damage assessment in beams under moving load.

Authors :
Toan Pham-Bao
Vien Le-Ngoc
Source :
Fracture & Structural Integrity / Frattura ed Integrità Strutturale. Oct2024, Issue 70, p55-70. 16p.
Publication Year :
2024

Abstract

This scientific paper explores the use of correlation coefficients of vibration signals and machine learning algorithms for structural damage assessment in beams under moving loads. The paper discusses the challenges of maintaining structural integrity and the importance of automated, nondestructive monitoring techniques. Preprocessing techniques, such as the random decrement technique (RDT), are highlighted for improving data analysis. Machine learning algorithms are identified as valuable tools for structural damage assessment. The paper concludes by emphasizing the potential of machine learning in safeguarding critical infrastructures. The text also discusses trigger points and the vibration response of a slender beam under a moving load. An artificial neural network (ANN) is proposed as a computational model for identifying non-linear features. Experimental testing on a simulated bridge girder using accelerometers collected data to identify and locate damage in the beam. The ANN achieved high accuracy in detecting damage appearance and location, but further research is needed to improve accuracy in real-world situations. [Extracted from the article]

Details

Language :
English
ISSN :
19718993
Issue :
70
Database :
Academic Search Index
Journal :
Fracture & Structural Integrity / Frattura ed Integrità Strutturale
Publication Type :
Academic Journal
Accession number :
179865039
Full Text :
https://doi.org/10.3221/IGF-ESIS.70.03