Rapid mechanical evaluation of quadrangular steel plates subjected to localised blast loadings

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dc.contributor.authorBortolan Neto, Len_AU
dc.contributor.authorSaleh, Men_AU
dc.contributor.authorPickerd, Ven_AU
dc.contributor.authorYiannakopoulos, Gen_AU
dc.contributor.authorMathys, Zen_AU
dc.contributor.authorReid, Wen_AU
dc.date.accessioned2023-05-16T10:36:22Zen_AU
dc.date.available2023-05-16T10:36:22Zen_AU
dc.date.issued2020-03en_AU
dc.date.statistics2023-05-04en_AU
dc.description.abstractThe design of modern military and naval platforms against weapon threats is often assisted by a combination of experimental, analytical and computational simulations. These tools provide relevant insights about material reliability, mechanical performance and platform design vulnerability to support the determination of safety critical aspects, such as response to blast and fragmentation loading. Analytical models are inherently simplified, limiting their ability to accurately model scenarios with complicated geometries and material properties, or highly non-linear loadings. Appropriate experimental and numerical modelling can overcome the limitations of analytical models but also require long lead times and high associated costs. These issues can be a point of concern for projects with strict development schedules, short time-to-solution, and limited resources. Machine learning techniques have proven viable in the development of fast-running models for highly non-linear problems. The present work explores four models based on the Multilayer Perceptron (MLP), a type of Artificial Neural Network (ANN), for assessing the mechanical response of mild steel plates subjected to localised blast loading. Experiments combined with validated Finite Element Analysis (FEA) models provide a hybrid dataset for training ANNs. The resultant dataset is a combination of sparsely populated experimental data with a denser dataset of validated FEA simulations. The final results demonstrate the potential of ANNs to incorporate high strain-rate material response behaviour, such as that from blast loading, into optimised models that can yield timely predictions of structural response. Crown Copyright © 2019 Published by Elsevier Ltd.en_AU
dc.identifier.articlenumber103461en_AU
dc.identifier.citationBortolan Neto, L., Saleh, M., Pickerd, V., Yiannakopoulos, G., Mathys, Z., & Reid, W. (2020). Rapid mechanical evaluation of quadrangular steel plates subjected to localised blast loadings. International Journal of Impact Engineering, 137, 103461.doi:10.1016/j.ijimpeng.2019.103461en_AU
dc.identifier.issn0734-743Xen_AU
dc.identifier.journaltitleInternational Journal of Impact Engineeringen_AU
dc.identifier.urihttps://doi.org/10.1016/j.ijimpeng.2019.103461en_AU
dc.identifier.urihttps://apo.ansto.gov.au/handle/10238/15036en_AU
dc.identifier.volume137en_AU
dc.language.isoenen_AU
dc.publisherElsevieren_AU
dc.subjectVulnerabilityen_AU
dc.subjectNeural networksen_AU
dc.subjectFinite element methoden_AU
dc.subjectStrain rateen_AU
dc.subjectExplosionsen_AU
dc.subjectSteelsen_AU
dc.subjectMilitary equipmenten_AU
dc.subjectSimulationen_AU
dc.titleRapid mechanical evaluation of quadrangular steel plates subjected to localised blast loadingsen_AU
dc.typeJournal Articleen_AU
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