Statistical Prediction of Concrete Compressive Strength Using Multiple Linear and Nonlinear Regression Models Developed in SPSS
DOI:
https://doi.org/10.65419/albahit.v5i3.162Keywords:
concrete compressive strength, SPSS, multiple linear regression, correlation analysis, nonlinear regression, model validation, water-cement ratioAbstract
Compressive strength is crucial for assessing concrete quality, but traditional methods require a 28-day curing period. This study develops user-friendly statistical models for early strength prediction using SPSS software. Analyzing a database of 431 concrete mixtures (381 for calibration, 50 for validation), the study evaluated six key predictors. The full six-variable linear model achieved an R² of 0.628, while a non-linear log-age model improved fit to R² = 0.650 but lacked generalizability. Independent validation showed a 32–38% error margin due to dataset heterogeneity. Ultimately, cement content and water-cement ratio proved to be the most critical factors. The study concludes that SPSS-based regression provides a cost-effective early assessment tool, though more homogeneous datasets are needed for engineering-grade accuracy.
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