Enhancing Concrete Strength Prediction from Non-Destructive Testing Under Variable Curing Temperatures Using Artificial Neural Networks

Non-destructive testing (NDT) methods are widely used to evaluate the performance of concrete, but their accuracy can be influenced by external factors such as curing temperature. Temperature not only modifies hydration kinetics and strength development but may also change the correlation between NDT measurements and compressive strength. However, no prior research has systematically examined how different curing temperatures influence the reliability of various NDT techniques. This study evaluates three curing temperatures and their effect on the correlation between NDTs and compressive strength at various ages (1, 3, 7, 28, and 90 days). Both simple regression analysis and artificial neural networks (ANNs) were employed to predict strength from NDT measurements. Results show that NDT sensitivity to curing temperature is most pronounced at early ages, and that linear regression models cannot adequately capture the complexity of these relationships. In contrast, ANNs demonstrated superior predictive capability, though initial training with limited data led to overfitting and instability. By applying Gaussian Noise Augmentation (GNA), model accuracy and generalization improved substantially, achieving R2 values above 0.95 across training, validation, and test sets. These findings highlight the potential of non-linear models, supported by data augmentation, to improve prediction reliability, lower experimental costs, and more accurately capture the role of curing temperature in NDT–strength correlations for concrete.