Investigating the Influence of Cement and Additive Normalization on Concrete Compressive Strength: A Statistical Analysis

Predicting the compressive strength of concrete presents a significant challenge due to its complex composition. Traditional approaches often grapple with data uncertainty, hindering accurate predictions. This study introduces an innovative methodology that integrates advanced preprocessing techniques with a suite of sophisticated prediction models. Employing a robust dataset from the UC Irvine Machine Learning Repository, comprising 1030 samples with eight distinct input variables, the methodology synergizes statistical methods, Bayesian analysis, and cutting-edge machine learning algorithms. Central to our approach is the application of normalization strategies Box-Cox transformation. These predictions were combined with Gaussian Process Regression (GPR), Multiple Linear Regression (MLR), and Support Vector Regression (SVR) to enhance predictive accuracy. The performance of these models is meticulously evaluated using a comprehensive set of metrics, including Root Mean Square Error (RMSE), R-square, Mean Absolute Error (MAE), and Mean Square Error (MSE). Our findings reveal a marked improvement in prediction accuracy, with GPR emerging as the most effective model. This study not only advances our understanding of concrete's compressive strength but also sets a precedent for employing a multi-faceted analytical approach in material science research.