Determination of compressive strength of perlite-containing slag-based geopolymers and its prediction using artificial neural network and regression-based methods

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dc.authoridSevim, Ozer/0000-0001-8535-2344
dc.authoridKorkmaz, Serdar/0000-0002-4247-3813
dc.contributor.authorAlakara, Erdinc H.
dc.contributor.authorNacar, Sinan
dc.contributor.authorSevim, Ozer
dc.contributor.authorKorkmaz, Serdar
dc.contributor.authorDemir, Ilhami
dc.date.accessioned2025-01-21T16:37:26Z
dc.date.available2025-01-21T16:37:26Z
dc.date.issued2022
dc.departmentKırıkkale Üniversitesi
dc.description.abstractThis study has two main objectives: (i) to investigate the parameters affecting the compressive strength (CS) of perlite-containing slag-based geopolymers and (ii) to predict the CS values obtained from experimental studies. In this regard, 540 cubic geopolymer samples incorporating different raw perlite powder (RPP) replacement ratios, different sodium hydroxide (NaOH) molarity, different curing time, and different curing temperatures for a total of 180 mixture groups were produced and their CS results were experimentally determined. Then conventional regression analysis (CRA), multivariate adaptive regression splines (MARS), and TreeNet methods, as well as artificial neural network (ANN) methods, were used to predict the CS results of geopolymers using this experimentally obtained data set. Root mean square error (RMSE), mean absolute error (MAE), scatter index (SI) and Nash-Sutcliffe (NS) performance statistics were used to evaluate the CS prediction capabilities of the methods. As a result, it was determined that the optimum molarity, curing time, and curing temperature were 14 M, 24 h, and 110 celcius, respectively and 48 h of heat curing did not have a significant effect on increasing the CS of the geopolymers. The highest performances in regression-based models were obtained from the MARS method. However, the ANN method showed higher prediction performance than the regression-based methods. Considering the RMSE values, it was seen that the ANN method made improvements by 24.7, 2.1, and 13.7 %, respectively, compared to the MARS method for training, validation, and test sets.
dc.description.sponsorship[2021/112]
dc.description.sponsorshipThe fourth and fifth authors gratefully acknowledge the financial assistance of the Kirikkale University Scientific Research Centre pro- vided under Project: 2021/112.
dc.identifier.doi10.1016/j.conbuildmat.2022.129518
dc.identifier.issn0950-0618
dc.identifier.issn1879-0526
dc.identifier.scopus2-s2.0-85140472829
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.conbuildmat.2022.129518
dc.identifier.urihttps://hdl.handle.net/20.500.12587/24479
dc.identifier.volume359
dc.identifier.wosWOS:000882193200002
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Sci Ltd
dc.relation.ispartofConstruction and Building Materials
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241229
dc.subjectGeopolymer; Compressive strength; Perlite powder; Artificial neural network (ANN); Multivariate adaptive regression splines; (MARS); Conventional regression analysis (CRA)
dc.titleDetermination of compressive strength of perlite-containing slag-based geopolymers and its prediction using artificial neural network and regression-based methods
dc.typeArticle

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