A multilevel Bayesian method for ultrasound-based damage identification in composite laminates

Abstract

This version is available at https://strathprints.strath.ac.uk/65462/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Abstract Estimating deterministic single-valued damage parameters when evaluating the actual health state of a material has a limited meaning if one considers not only the existence of measurement errors, but also that the model chosen to represent the damage behavior is just an idealization of reality. This paper proposes a multilevel Bayesian inverse problem framework to deal with these sources of uncertainty in the context of ultrasound-based damage identification. Although the methodology has a broad spectrum of applicability, here it is oriented to model-based damage assessment in layered composite materials using through-transmission ultrasonic measurements. The overall procedure is first validated on synthetically generated signals and then evaluated on real signals obtained from a post-impact fatigue damage experiment in a cross-ply carbon-epoxy laminate. The evidence of the hypothesized model of damage is revealed as a suitable measure of the overall ability of that candidate hypothesis to represent the actual damage state observed by the ultrasound, thus avoiding the extremes of over-fitting or under-fitting the ultrasonic signal.

A Sequential Algorithm for Signal Segmentation

“…Experiments show that the main results are unaltered if we use samples of size 5000 instead of 10000. Also, there are other methods to obtain samples from a posterior distribution, in particular the Approximate Bayes Computation techniques [Chiachio (2014)] ] [Beck (2014B)] [Chiachio (2017)], that might help reducing the computation time, specially in the FBST step. We are currently investigating these possibilities.…”

A Sequential Algorithm for Signal Segmentation

“…Experiments show that the main results are unaltered if we use samples of size 5000 instead of . Also, there are other methods to obtain samples from a posterior distribution, in particular the Approximate Bayes Computation techniques [ 26 , 27 , 28 , 29 ], that might help reduce the computation time, especially in the FBST step. We are currently investigating these possibilities.…”

Non-Destructive Testing of Low-Velocity Impacted Composite Material Laminates through Ultrasonic Inspection Methods

“…The UT response signals were utilised for the validation of the developed digital signal model in order to obtain the damage identification. In [48], a multi-level Bayesian method was utilised to identify the through-the-thickness position and the effective mechanical properties of the damaged layers in the same composite laminates using through-transmission UT measurements.…”