Bayesian analysis of acoustic emission data for prediction of fatigue crack growth in concrete

Radhika, V and Chandra Kishen, JM (2024) Bayesian analysis of acoustic emission data for prediction of fatigue crack growth in concrete. In: Theoretical and Applied Fracture Mechanics, 131 .

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Abstract

Acoustic emission (AE) is a valuable technique for non-destructive testing, enabling real-time monitoring and measurement of fatigue crack growth in engineering structures. The present study focuses on investigating the fatigue crack growth behaviour in plain concrete specimens through Bayesian analysis of AE data collected during the testing of beam specimens under three-point bending. A log-linear relationship is established between parameters extracted from AE waveforms and the fatigue crack growth in concrete. Additionally, AE energy is identified as the most suitable parameter for characterising the fatigue behaviour of concrete. Bayesian regression is employed for estimating model parameters and their posterior distributions. The proposed model is validated through post-processing and Bayesian analysis of experimental data from the literature. Furthermore, it is demonstrated that the estimated model parameters remain unaffected by the frequency of fatigue loading and the size of the specimen. © 2024 Elsevier Ltd

Item Type:	Journal Article
Publication:	Theoretical and Applied Fracture Mechanics
Publisher:	Elsevier B.V.
Additional Information:	The copyright for this article belongs to Elsevier B.V.
Keywords:	Acoustic emission testing; Concrete testing; Concretes; Parameter estimation, Acoustic emission data; Acoustic-emissions; Bayesian Analysis; Bayesian regression; Cracks propagation; Fatigue of concrete; Modeling parameters; Non destructive testing; Real time measurements; Real time monitoring, Fatigue crack propagation
Department/Centre:	Division of Mechanical Sciences > Civil Engineering
Date Deposited:	20 May 2024 11:49
Last Modified:	20 May 2024 11:49
URI:	https://eprints.iisc.ac.in/id/eprint/84755

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