A novel hybrid metaheuristic algorithm and response surface methodology approach for predictive modeling and optimization of cementitious compressive strength
Year: 2025
Type: article
Abstract: Compressive strength is a fundamental property for evaluating cementitious materials, yet its prediction and optimization remain challenging due to the complex and nonlinear interactions of chemical composition, curing time, and processing conditions. This study presents a hybrid framework that integrates Response Surface Methodology (RSM) with the Genetic Algorithm (GA) to enhance predictive modeling and optimize cement mixtures. RSM was used to construct polynomial models—linear, linear + square, linear + interaction, and full quadratic—to capture the relationships between eight oxide compositions (Silo₂, Allow₃, Foo₃, Cano, Mg, SO₃, K₂O, NaoSiO 2 ,Al 2 O 3 ,Fe 2 O 3 ,CaO,MgO,SO 3 ,K 2 O,Na 2 O) and compressive strength at 2, 7, and 28 days. The Genetic Algorithm was employed to explore this high-dimensional solution space and identify optimal compositions. Experimental data from a regional cement producer /laboratory were used for model training and validation. Results indicate that the full Quadratic model consistently outperformed other RSM models, achieving the lowest RMSE and highest R² across all curing periods. GA optimization further improved performance, with maximum predicted compressive strengths increasing by 83% at 2 days, 48% at 7 days, and 43% at 28 days compared to RSM-only predictions. These findings highlight the robustness of the hybrid GA + RSM approach in capturing nonlinearities, improving accuracy, and achieving superior optimization outcomes. The framework offers a promising tool for developing eco-efficient and high-strength cementitious mixtures, contributing to innovation in sustainable construction materials.
Source: Scientific Reports
Authors: Rawa Saman Maaroof, Akhterkhan Saber Hamad, Mohammad Mahmood Faqe Hussein
Institution: University of Sulaimani
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
OpenAlex ID: W4417420531