The Influence of the Water–Cement Ratio on Concrete Resistivity: A Temperature and Saturation Dependent Analysis Using an Experimental and Predictive Approach

Concrete resistivity is a critical parameter for assessing durability and monitoring the structural health of reinforced concrete. This study systematically evaluates the effects of the water-to-cement (w/c) ratio, saturation ratio (SR), and temperature on concrete resistivity using three different predictive models: linear regression, cubic Support Vector Machine (SVM), and Gaussian Process Regression (GPR). Each model was independently trained and tested to assess its ability to capture the nonlinear relationships between these key parameters. Experimental results show that resistivity decreases significantly under increasing load due to geometrical effects. For a w/c ratio of 0.4, resistivity decreases by −12.48% at 100% SR and by −6.68% at 60% SR under 20% loading. Higher w/c ratios (0.5 and 0.6) exhibit more pronounced resistivity reductions due to increased porosity and ion mobility, with a maximum decrease of −13.68% for w/c = 0.6. Among the developed predictive models, the Matern 5/2 Gaussian process regression (GPR) model demonstrated the highest accuracy, achieving an RMSE of 5.21, R² of 0.99, MSE of 27.19, and MAE of 3.40, significantly outperforming the other approaches. Additionally, a permutation importance analysis revealed that the saturation ratio (SR) is the most critical variable influencing resistivity, followed by the water–cement ratio, while temperature has the least impact. These findings provide valuable insights into the durability assessment and corrosion prevention of reinforced concrete, offering practical implications for the optimization of material design and structural health monitoring in civil engineering.