Mimar Sinan Güzel Sanatlar Üniversitesi Açık Bilim, Sanat Arşivi

Açık Bilim, Sanat Arşivi, Mimar Sinan Güzel Sanatlar Üniversitesi tarafından doğrudan ve dolaylı olarak yayınlanan; kitap, makale, tez, bildiri, rapor gibi tüm akademik kaynakları uluslararası standartlarda dijital ortamda depolar, Üniversitenin akademik performansını izlemeye aracılık eder, kaynakları uzun süreli saklar ve yayınların etkisini artırmak için telif haklarına uygun olarak Açık Erişime sunar.

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dc.contributor.authorOcak, Ayla
dc.contributor.authorBekdaş, Gebrail
dc.contributor.authorIşıkdağ, Ümit
dc.contributor.authorNigdeli, Sinan Melih
dc.contributor.authorBilir, Turhan
dc.date.accessioned2024-11-06T07:48:32Z
dc.date.available2024-11-06T07:48:32Z
dc.date.issued2024en_US
dc.identifier.citationOcak, A., Bekdaş, G., Işıkdağ, Ü., Nigdeli, S. M., & Bilir, T. (2024). Drying shrinkage and crack width prediction using machine learning in mortars containing different types of industrial by-product fine aggregates. Journal of Building Engineering, 97. https://doi.org/10.1016/j.jobe.2024.110737en_US
dc.identifier.issn2352-7102
dc.identifier.urihttps://doi.org/10.1016/j.jobe.2024.110737
dc.identifier.urihttps://hdl.handle.net/20.500.14124/6642
dc.description.abstractConcrete is a material that loses water and changes shape while hardening due to its structure. Over time, this water loss results in some shrinkage of the hardened concrete, referred to as drying shrinkage. In addition, water loss of concrete also causes the formation of various cracks. The aggregate used in concrete plays an important role in the shrinkage and cracking of concrete. The focus of this study is to accurately estimate the amount of crack width and drying shrinkage over time after the substitution of fine aggregates with other types of aggregates (consisting of various industrial by-products or wastes at different percentages) in the concrete mortar. For this purpose, various experimental results of the ‘substituted fine aggregate concrete mortars’ were converted into a data set. Following this a model was developed to predict the drying shrinkage and crack width of concrete mortars. The machine learning model was trained with the measurement results of 60-day drying shrinkage and crack widths of concrete mortars with different proportions of bottom ash (BA), granulated blast furnace slag (GBFS), fly ash (FA), and crushed tiles (CT). To enhance the detection/prediction capability of the model, the model hyperparameters were optimized. It is observed that the developed model was able to detect the drying shrinkage and crack width with an accuracy exceeding 99.6 %. In addition, the physical properties such as grain shape (angular or round) of components like fine aggregates may be effective for improved performance of the machine learning models in predictions of the drying shrinkage values or drying shrinkage cracking widths. © 2024 Elsevier Ltden_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.ispartofJournal of Building Engineeringen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCracken_US
dc.subjectDrying shrinkageen_US
dc.subjectHyperparameter optimizationen_US
dc.subjectMachine learningen_US
dc.titleDrying shrinkage and crack width prediction using machine learning in mortars containing different types of industrial by-product fine aggregatesen_US
dc.typearticleen_US
dc.authorid0000-0002-2660-0106en_US
dc.departmentFakülteler, Mimarlık Fakültesi, Mimarlık Bölümüen_US
dc.institutionauthorIşıkdağ, Ümit
dc.identifier.doi10.1016/j.jobe.2024.110737en_US
dc.identifier.volume97en_US
dc.identifier.issueArticle number 110737en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.authorwosidA-3306-2012en_US
dc.authorscopusid25223356600en_US
dc.identifier.wosqualityQ1en_US
dc.identifier.wos001317975500001en_US
dc.identifier.scopus2-s2.0-85203875199en_US


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