Hybrid deep learning model for concrete incorporating microencapsulated phase change materials

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dc.authoridTurk, Kazim/0000-0002-6314-9465
dc.authoridMarani, Afshin/0000-0001-5858-6153
dc.authoridTANYILDIZI, Harun/0000-0002-7585-2609
dc.authoridNehdi, Moncef/0000-0002-2561-993X
dc.authorwosidTurk, Kazim/AAB-7513-2019
dc.authorwosidMarani, Afshin/AAW-3350-2021
dc.authorwosidTANYILDIZI, Harun/A-1950-2016
dc.authorwosidNehdi, Moncef/P-3725-2019
dc.contributor.authorTanyildizi, Harun
dc.contributor.authorMarani, Afshin
dc.contributor.authorTurk, Kazim
dc.contributor.authorNehdi, Moncef L.
dc.date.accessioned2024-08-04T20:51:35Z
dc.date.available2024-08-04T20:51:35Z
dc.date.issued2022
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThe inclusion of microencapsulated phase change materials (MPCMs) in concrete promotes thermal energy storage, thus enhancing sustainable design. Notwithstanding this advantage, the compressive strength of concrete dramatically decreases upon MPCM addition. While several experimental studies have explored the origin of this compressive strength reduction, a reliable and practical framework for the prediction of the compressive strength of MPCM-integrated concrete is yet to be developed. The current research proposes a deep learning approach to estimate the compressive strength of MPCM-integrated cementitious composites based on its mixture proportions and the thermophysical properties of PCM. Extreme learning machines (ELMs), autoencoders, hybrid ELM-autoencoder, and extreme gradient boosting (XGBoost) models were purposefully developed using the largest pertinent experimental dataset available to date encompassing 244 mixture design examples retrieved from the open literature. The results demonstrate the capability of the hybrid deep learning and XGBoost models in accurately modeling the compressive strength of PCM integrated concrete with favorably low prediction error. Furthermore, a sensitivity analysis identified the most influential parameters on the compressive strength development to assist the mixture design of concrete incorporating MPCM.en_US
dc.identifier.doi10.1016/j.conbuildmat.2021.126146
dc.identifier.issn0950-0618
dc.identifier.issn1879-0526
dc.identifier.scopus2-s2.0-85122461103en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.conbuildmat.2021.126146
dc.identifier.urihttps://hdl.handle.net/11616/100400
dc.identifier.volume319en_US
dc.identifier.wosWOS:000736980000001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofConstruction and Building Materialsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectPhase change materialen_US
dc.subjectConcreteen_US
dc.subjectCompressive strengthen_US
dc.subjectDeep learningen_US
dc.subjectExtreme learning machineen_US
dc.subjectAutoencoderen_US
dc.subjectExtreme gradient boostingen_US
dc.subjectSensitivity analysisen_US
dc.titleHybrid deep learning model for concrete incorporating microencapsulated phase change materialsen_US
dc.typeArticleen_US

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