Intelligent design framework for compressive strength modeling of ultra-high-performance geopolymer concrete (UHPGC): grey wolf optimizer-integrated machine learning and experimental verification

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dc.contributor.authorKatlav, Metin
dc.contributor.authorTurk, Kazim
dc.date.accessioned2026-04-04T13:33:32Z
dc.date.available2026-04-04T13:33:32Z
dc.date.issued2026
dc.departmentİnönü Üniversitesi
dc.description.abstractUltra-high-performance concrete (UHPC) provides superior strength and durability but suffers from high cost and environmental impact. As a sustainable alternative, Ultra-High-Performance Geopolymer Concrete (UHPGC) requires reliable tools for predicting compressive strength (CS), yet existing frameworks remain limited, especially those combining AI, explainability, and experimental verification. This paper develops a Grey Wolf Optimizer (GWO)-enhanced machine learning framework to predict the CS of UHPGC using 179 mixes compiled from the literature. Four GWO-ML models (CatBoost, GBM, RF, ETR) were trained, with GWO-CatBoost achieving the highest performance (R2 = 0.971), followed by GWO-GBM (R2 = 0.967). SHAP-based analysis identified age, fiber, SF, SFL, and Na2SiO3 as the most influential variables. ICE and PDPs provided optimal design ranges for engineering use. A user-friendly GUI was also developed to predict CS along with cost and carbon footprint. Experimental tests on 10 new mixtures confirmed strong generalization of the GWO-CatBoost model (R2 = 0.884).
dc.identifier.doi10.1080/21650373.2026.2629850
dc.identifier.issn2165-0373
dc.identifier.issn2165-0381
dc.identifier.orcid0000-0001-9093-7195
dc.identifier.scopus2-s2.0-105030249622
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1080/21650373.2026.2629850
dc.identifier.urihttps://hdl.handle.net/11616/109215
dc.identifier.wosWOS:001692018500001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherTaylor & Francis Ltd
dc.relation.ispartofJournal of Sustainable Cement-Based Materials
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250329
dc.subjectUltra-high-performance geopolymer concrete (UHPGC)
dc.subjectmachine learning
dc.subjectcompressive strength
dc.subjectexplainable artificial intelligence (XAI)
dc.titleIntelligent design framework for compressive strength modeling of ultra-high-performance geopolymer concrete (UHPGC): grey wolf optimizer-integrated machine learning and experimental verification
dc.typeArticle

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