AI-driven design for the compressive strength of ultra-high performance geopolymer concrete (UHPGC): From explainable ensemble models to the graphical user interface

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dc.authorscopusid57845945600
dc.authorscopusid58776617800
dc.authorscopusid57222370083
dc.contributor.authorKatlav M.
dc.contributor.authorErgen F.
dc.contributor.authorDonmez I.
dc.date.accessioned2024-08-04T20:03:40Z
dc.date.available2024-08-04T20:03:40Z
dc.date.issued2024
dc.departmentİnönü Üniversitesien_US
dc.description.abstractUltra-high performance geopolymer concrete (UHPGC) has become of interest in recent years as a more economical and sustainable alternative while offering similar mechanical performance to ordinary ultra-high performance concrete (UHPC). The lack of an effective mix design methodology has inhibited the widespread use of UHPGC, despite its potential. This paper adopted an artificial intelligence (AI)-based approach to accurately model the compressive strength (CS) of UHPGC, a critical parameter to ensure structural integrity and reliability. Ensemble machine learning (ML) models such as RF, XGBoost, LightGBM and AdaBoost, which have been very popular lately, were selected as AI algorithms. For the establishment of these models, a comprehensive and reliable dataset of 181 test results was used, including 13 input features. Additionally, feature importance and Shapley additive explanations (SHAP) analyses were used to ensure the explainability of the prediction models and tackle the “black box” challenge of ML models. The results obtained revealed that all ensemble models successfully predicted the CS of UHPGC; in particular, the XGBoost model consistently exhibited the best overall performance in terms of higher R2 (0.948) and lower RMSE (6.68), MAE (4.73), MAPE (4 %), and mean error value (1.095), in the test phase. Moreover, feature importance and SHAP analyses revealed that the most influential features on the CS of UHPGC were age, fiber and silica fume, sodium silicate (Na2SiO3), and water content. Lastly, a graphical user interface (GUI) was developed to easily predict the CS of UHPGC in practical applications. © 2024 Elsevier Ltden_US
dc.identifier.doi10.1016/j.mtcomm.2024.109915
dc.identifier.issn2352-4928
dc.identifier.scopus2-s2.0-85199388068en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1016/j.mtcomm.2024.109915
dc.identifier.urihttps://hdl.handle.net/11616/92003
dc.identifier.volume40en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.relation.ispartofMaterials Today Communicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCompressive strengthen_US
dc.subjectGraphical user interfaceen_US
dc.subjectMachine learningen_US
dc.subjectShapley additive explanationsen_US
dc.subjectUltra-high performance geopolymer concreteen_US
dc.titleAI-driven design for the compressive strength of ultra-high performance geopolymer concrete (UHPGC): From explainable ensemble models to the graphical user interfaceen_US
dc.typeArticleen_US

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