High-Early-Strength Concrete (HESC) is increasingly required in accelerated construction, yet most existing studies focus on single nano-additives rather than hybrid waste-derived systems. This study investigates the individual and combined effects of nanoclay (NC), nanosilica (NS), and cellulose nanofibers (NCel)—each produced from industrial or agricultural waste—on the mechanical and microstructural properties of HESC. A Box–Behnken response surface methodology (RSM) design was employed to optimize nanomaterial dosages with respect to early-age compressive strength, while microstructural evaluation (SEM, EDS, elemental mapping) clarified the mechanisms of enhancement. The results demonstrate that NC, NS, and NCel play complementary roles in hydration acceleration, particle packing, pore refinement, and crack-bridging. The optimized hybrid system (1.64% NC, 0.115% NS, 0.027% NCel) achieved a 3-day compressive strength of 59.7 MPa, 7-day strength of 71.2 MPa, and 28-day strength of 94.6 MPa, representing increases of 42.14%, 36.92%, and 21.59%, respectively, over the control mixture. Microstructural observations confirmed matrix densification, reduced Ca/Si ratio (from 2.05 to 1.68), refined pore structure (<0.4 μm vs. 0.9–1.2 μm in control), and enhanced ITZ in the optimized mixtures. Statistical analysis yielded robust predictive models (R2 = 0.977–0.996) with significant interaction terms confirming synergistic effects among the three nanomaterials. This work demonstrates that waste-derived hybrid nano-systems offer a sustainable and effective strategy for producing high-performance HESC, with the RSM-derived optimum providing balanced early- and later-age strength while maintaining practical feasibility for field implementation.
Nehal Hamed, Mohamed K. Ismail, M. Serag et al.· Sustainability· 0 citations
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.
A. Sabry, Sabry A. Ahmed, Mohamed K. Ismail et al.· Discover Materials· 0 citations