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dc.contributor.authorZahedi, Mona
dc.contributor.authorGolchin, Shahriar
dc.date.accessioned2023-01-06T01:06:01Z
dc.date.available2023-01-06T01:06:01Z
dc.date.issued2022-12-06
dc.identifier.citationZahedi, M., & Golchin, S. (2022). Prediction of blast loading on protruded structures using machine learning methods. International Journal of Protective Structures.en_US
dc.identifier.issn2041-4196
dc.identifier.doi10.1177/20414196221144067
dc.identifier.urihttp://hdl.handle.net/10150/667329
dc.description.abstractCurrent empirical and semi-empirical based design manuals are restricted to the analysis of simple building configurations against blast loading. Prediction of blast loads for complex geometries is typically carried out with computational fluid dynamics solvers, which are known for their high computational cost. The combination of high-fidelity simulations with machine learning tools may significantly accelerate processing time, but the efficacy of such tools must be investigated. The present study evaluates various machine learning algorithms to predict peak overpressure and impulse on a protruded structure exposed to blast loading. A dataset with over 250,000 data points extracted from ProSAir simulations is used to train, validate, and test the models. Among the machine learning algorithms, gradient boosting models outperformed neural networks, demonstrating high predictive power. These models required significantly less time for hyperparameter optimization, and the randomized search approach achieved relatively similar results to that of grid search. Based on permutation feature importance studies, the protrusion length was considered a significantly more influential parameter in the construction of decision trees than building height.en_US
dc.language.isoenen_US
dc.publisherSAGE Publicationsen_US
dc.rights© The Author(s) 2022.en_US
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en_US
dc.subjectartificial neural networksen_US
dc.subjectBlast loadingen_US
dc.subjectlinear regressionen_US
dc.subjectmachine learningen_US
dc.subjecttree-based modelsen_US
dc.titlePrediction of blast loading on protruded structures using machine learning methodsen_US
dc.typeArticleen_US
dc.identifier.eissn2041-420X
dc.contributor.departmentDepartment of Computer Science, University of Arizonaen_US
dc.identifier.journalInternational Journal of Protective Structuresen_US
dc.description.noteImmediate accessen_US
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en_US
dc.eprint.versionFinal accepted manuscripten_US
dc.identifier.pii10.1177/20414196221144067
dc.source.journaltitleInternational Journal of Protective Structures
dc.source.beginpage204141962211440
refterms.dateFOA2023-01-06T01:06:02Z


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