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dc.contributor.authorKandiri, Amirreza
dc.contributor.authorSartipi, Farid
dc.contributor.authorKioumarsi, Mahdi
dc.date.accessioned2021-01-29T23:33:30Z
dc.date.accessioned2021-03-08T10:21:26Z
dc.date.available2021-01-29T23:33:30Z
dc.date.available2021-03-08T10:21:26Z
dc.date.issued2021-01-06
dc.identifier.citationKandiri, Sartipi, Kioumarsi M. Predicting Compressive Strength of Concrete Containing Recycled Aggregate Using Modified ANN with Different Optimization Algorithms. Applied Sciences. 2021;11(2)en
dc.identifier.issn2076-3417
dc.identifier.urihttps://hdl.handle.net/10642/9911
dc.description.abstractUsing recycled aggregate in concrete is one of the best ways to reduce construction pollu- tion and prevent the exploitation of natural resources to provide the needed aggregate. However, recycled aggregates affect the mechanical properties of concrete, but the existing information on the subject is less than what the industry needs. Compressive strength, on the other hand, is the most important mechanical property of concrete. Therefore, having predictive models to provide the re- quired information can be helpful to convince the industry to increase the use of recycled aggregate in concrete. In this research, three different optimization algorithms including genetic algorithm (GA), salp swarm algorithm (SSA), and grasshopper optimization algorithm (GOA) are employed to be hybridized with artificial neural network (ANN) separately to predict the compressive strength of concrete containing recycled aggregate, and a M5P tree model is used to test the effi- ciency of the ANNs. The results of this study show the superior efficiency of the modified ANN with SSA when compared to other models. However, the statistical indicators of the hybrid ANNs with SSA, GA, and GOA are so close to each other.en
dc.language.isoenen
dc.publisherMDPIen
dc.relation.ispartofseriesApplied Sciences;Volume 11, Issue 2
dc.rightsCreative Commons Attribution 4.0 International (CC BY 4.0) licenseen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectConcreteen
dc.subjectCompressive strengthen
dc.subjectArtificial neural networksen
dc.subjectGenetic algorithmsen
dc.subjectSalp swarm algorithmsen
dc.subjectGrasshopper optimization algorithmsen
dc.subjectM5P treesen
dc.titlePredicting Compressive Strength of Concrete Containing Recycled Aggregate Using Modified ANN with Different Optimization Algorithmsen
dc.typeJournal articleen
dc.typePeer revieweden
dc.date.updated2021-01-29T23:33:30Z
dc.description.versionpublishedVersionen
dc.identifier.doihttps://doi.org/10.3390/app11020485
dc.identifier.cristin1883097
dc.source.journalApplied Sciences


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Except where otherwise noted, this item's license is described as Creative Commons Attribution 4.0 International (CC BY 4.0) license