Prediction of blast loading on protruded structures using machine learning methods
dc.contributor.author | Zahedi, Mona | |
dc.contributor.author | Golchin, Shahriar | |
dc.date.accessioned | 2023-01-06T01:06:01Z | |
dc.date.available | 2023-01-06T01:06:01Z | |
dc.date.issued | 2022-12-06 | |
dc.identifier.citation | Zahedi, M., & Golchin, S. (2022). Prediction of blast loading on protruded structures using machine learning methods. International Journal of Protective Structures. | en_US |
dc.identifier.issn | 2041-4196 | |
dc.identifier.doi | 10.1177/20414196221144067 | |
dc.identifier.uri | http://hdl.handle.net/10150/667329 | |
dc.description.abstract | Current 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.iso | en | en_US |
dc.publisher | SAGE Publications | en_US |
dc.rights | © The Author(s) 2022. | en_US |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en_US |
dc.subject | artificial neural networks | en_US |
dc.subject | Blast loading | en_US |
dc.subject | linear regression | en_US |
dc.subject | machine learning | en_US |
dc.subject | tree-based models | en_US |
dc.title | Prediction of blast loading on protruded structures using machine learning methods | en_US |
dc.type | Article | en_US |
dc.identifier.eissn | 2041-420X | |
dc.contributor.department | Department of Computer Science, University of Arizona | en_US |
dc.identifier.journal | International Journal of Protective Structures | en_US |
dc.description.note | Immediate access | en_US |
dc.description.collectioninformation | This 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.version | Final accepted manuscript | en_US |
dc.identifier.pii | 10.1177/20414196221144067 | |
dc.source.journaltitle | International Journal of Protective Structures | |
dc.source.beginpage | 204141962211440 | |
refterms.dateFOA | 2023-01-06T01:06:02Z |