DEVELOPMENT OF REGRESSION MODELS FOR PREDICTING THE STRENGTH PROPERTIES OF NORMAL AND POZZOLANIC CONCRETE
Abstract
Concrete strength prediction is important for improving mix design, quality control, and the effective utilization of supplementary cementitious materials. This study developed regression models for predicting the strength properties of normal and pozzolanic concrete incorporating rice husk ash (RHA) as a partial replacement for cement. Concrete was produced using a 1:2:4 mix proportion and a constant water-to-binder ratio of 0.58, with 10% of the cement replaced by RHA in the pozzolanic concrete. A total of 60 cubes (150 × 150 × 150 mm) were cast and cured for 28 and 56 days, while 30 cylinders (150 × 300 mm) were cured for 56 days. Specimens were tested for compressive and split tensile strength in accordance with British Standards. The RHA contained 80.70% SiO?, confirming its pozzolanic suitability. Regression analysis using SPSS produced strong predictive models with R² values of 0.996 and 0.998 for compressive strength and 0.951 and 0.994 for split tensile strength of normal and pozzolanic concrete, respectively. Pozzolanic concrete achieved 3.33% higher compressive strength and 9.68% higher split tensile strength than normal concrete at 56 days. Differences between observed and predicted strength values ranged from 1.23% to 16.86%. Cement was the most statistically significant predictor in all models (p = 0.000). The study concluded that regression analysis provides useful predictive models for the strength properties of normal and pozzolanic concrete and can support concrete mix assessment, performance prediction, and optimization of sustainable concrete incorporating agricultural waste materials.
Keywords: Regression models, concrete strength, normal concrete, pozzolanic concrete, rice husk ash
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