Prediction of Compressive Strength of Activated Binary Cement Mortars Using Response Surface Methodology and Artificial Neural Networks
Year: 2026
Type: article
Abstract: This study investigates the prediction of the compressive strength of activated binary cement mortars containing ground granulated blast-furnace slag (GGBFS) using two complementary modelling approaches: response surface methodology (RSM) and an artificial neural network (ANN). A database of 96 mix–curing combinations was assembled from the published literature on activated slag cement systems. Four input parameters were considered: slag replacement ratio (0–70 %), slag fineness (Blaine, 320–650 m²/kg), curing temperature (20–80 °C) and curing time (1–90 days). The output variable was the 1- to 90-day compressive strength (9–64 MPa). A second-order RSM polynomial was fitted and analysed by ANOVA, while a back-propagation feed-forward ANN with a 4–9–6–1 architecture and hyperbolic tangent activation was trained using the L-BFGS algorithm. The ANN reached R² = 0.996 and RMSE = 0.92 MPa over the whole dataset, clearly outperforming the RSM model (R² = 0.785, RMSE = 6.83 MPa). Sensitivity analysis showed that curing time was the dominant predictor (62 %), followed by slag fineness (22 %), curing temperature (8 %) and slag replacement ratio (8 %). The results confirm that ANN is a more reliable surrogate for the highly non-linear hydration kinetics of activated slag mortars and can be used to support mix-design decisions and reduce experimental effort.
Source: Materials Science
DOI: 10.5755/j02.ms.45203
Institutions: University Mohamed Boudiaf of M'sila, University of Bouira
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $539
OpenAlex ID: W7225419458