Geopolymer concrete (GPC) is a sustainable alternative to Portland cement concrete; however, complex geopolymerization mechanisms and nonlinear strength development under ambient curing make mixture optimization challenging. This study develops a chemistry-informed data-driven framework to predict the 28-day compressive strength of ambient-cured slag/fly ash–based GPC. A dataset of 151 mixtures was compiled incorporating eight input parameters, including key precursor oxide ratios (SiO₂/CaO, SiO₂/Al₂O₃, and CaO/Al₂O₃), which are rarely considered in existing predictive models. Artificial Neural Network (ANN) and Gene Expression Programming (GEP) models were developed and compared. The optimal ANN model (8–2–2–1 architecture) achieved superior predictive accuracy (R² = 0.93, MAE = 2.82), while the GEP model (R² = 0.77, MAE = 5.55) produced an explicit mathematical equation suitable for practical applications. Model reliability was verified experimentally using four new mix designs. Sensitivity analysis identified the SiO₂/CaO ratio as the most influential parameter governing strength development in ambient-cured GPC.
ABSTRACT Geopolymer concrete is a sustainable substitute for ordinary Portland cement which minimizes carbon dioxide emissions and effectively utilizes the waste from industries. Proper predictive estimating compressive strength can assist in the mix deign optimization, structural reliability. The article presents a ma...
This study proposes a material-centered predictive framework for fly ash (FA)–based cementitious composites by integrating mixture composition, early-age mechanical properties, and microstructural parameters to model the evolution of compressive strength (CS). A bio-inspired hybrid regression approach, the Enzyme Actio...
Handan Gürsoy-Demir, N. Özcan, Murat Ozturk· Materials Research Express· 0 citations
Given the environmental challenges posed by the production and disposal of industrial waste, reusing such materials in the construction industry, especially for the development of sustainable concrete, offers an eco-friendly solution and cost reduction. This study investigates the use of waste foundry sand (WFS) as a p...
Amir Khosrow Ghamari, Ali Seyedkazemi, Saba Jahangir et al.· Scientific Reports· 0 citations
Developing reliable computational tools for durability and service-life assessment of concrete structures in aggressive environments is essential for advancing predictive modeling in structural engineering. This study introduces machine learning (ML)–based models for forecasting the sulfate and acid resistance of recyc...
P. Singh, Puja Rajhans· Engineering Research Express· 0 citations
Portland cement production accounts for roughly 8% of anthropogenic CO2 emissions, driving interest in low-carbon geopolymer binders. One-part (“just-add-water”) geopolymers, which replace hazardous liquid activators with a dry, pre-blended solid activator, are especially suited to field deployment where handling safet...
