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