Guided wave-based characterisation of cracks in pipes utilising approximate Bayesian computation

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dc.contributor.authorZeng, Z.
dc.contributor.authorGao, M.
dc.contributor.authorNg, C.T.
dc.contributor.authorSheikh, A.H.
dc.date.issued2023
dc.descriptionAvailable online 29 August 2023
dc.description.abstractThis paper proposed a probabilistic framework of damage characterisation to detect and identify early-state cracks in pipe-like structures using ultrasonic guided waves. The crack location, crack sizes (e.g., depth and width of the crack), and Young’s modulus are considered as unknown parameters in the model updating using a Bayesian approach, by which their values and the associated uncertainties are quantified. The proposed framework is developed based on approximate Bayesian computation (ABC) by subset simulation, which is a likelihood-free Bayesian approach. This algorithm estimates the posterior distributions of unknown parameters by directly accessing the similarity between the measured signals from experiments and the simulated guided wave (GW) signals from the numerical model. In this case, the evaluation of likelihood functions can be smartly circumvented during Bayesian inference. A time-domain spectral finite element (SFE) method with a cracked finite element model is employed to model the pipes to enhance the computational efficiency of the simulation and model updating. Numerical and experimental case studies are carried out to evaluate the performance of the proposed likelihood-free approach. Numerical results show the accuracy and robustness of the proposed approach in identifying unknown parameters under different scenarios. The associated uncertainties of each parameter are also quantified by analysing the statistical properties of the sample set, such as mean and coefficient of variation (COV) values. Experimental results show that the proposed method can accurately identify the unknown parameters, which further verifies the accuracy and practicability of the probabilistic damage characterisation framework.
dc.description.statementofresponsibilityZijie Zeng, Min Gao, Ching Tai Ng, Abdul Hamid Sheikh
dc.identifier.citationThin Walled Structures, 2023; 192:111138-1-111138-14
dc.identifier.doi10.1016/j.tws.2023.111138
dc.identifier.issn0263-8231
dc.identifier.issn1879-3223
dc.identifier.orcidZeng, Z. [0000-0002-5797-8080]
dc.identifier.orcidGao, M. [0000-0003-0355-8651]
dc.identifier.orcidSheikh, A.H. [0000-0002-2839-1707]
dc.identifier.urihttps://hdl.handle.net/2440/139893
dc.language.isoen
dc.publisherElsevier
dc.relation.granthttp://purl.org/au-research/grants/arc/DP210103307
dc.relation.granthttp://purl.org/au-research/grants/arc/LP210100415
dc.rights© 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
dc.source.urihttps://doi.org/10.1016/j.tws.2023.111138
dc.subjectDamage identification; Pipe; Bayesian approach; Approximate Bayesian computation; Spectral finite element; Guided waves; Torsional wave
dc.titleGuided wave-based characterisation of cracks in pipes utilising approximate Bayesian computation
dc.typeJournal article
pubs.publication-statusPublished

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