Predictive Modeling of Concrete Water Penetration Depth Based on Material Properties

Permeability in concrete is a fundamental criterion for assessing its quality and its ability to resist environmental impacts. This research aims to develop an enhanced model for estimating water penetration depth into concrete using linear regression analysis. The regression analysis was conducted using the statistical analysis software SPSS. In this study, penetration depth is investigated as a dependent variable, while various concrete properties such as compressive strength, tensile strength, sorptivity, alkalinity, binder content, and water-cement ratio are examined as independent variables. Moreover, the concrete mixes utilized three different cementitious materials: Silica Fume, Slag, and Fly Ash. The results indicated that the Silica Fume group exhibited the lowest permeability, followed by the Slag group and then the Fly Ash group. Comparison of the developed model's results with those of previous studies demonstrates its high accuracy in estimating penetration depth in concrete. This study highlights the importance of using advanced statistical models, such as regression analysis, which can contribute to improving the quality and sustainability of concrete structures in different environments.