Development and validation for prediction model of critical chloride content in reinforced concrete using key material parameters
摘要
Critical chloride content (Ccr) serves as a quantitative indicator of the durability limit state of concrete structures and plays an essential role in both durability design and service life assessment. Conventional models for predicting the Ccr associated with steel depassivation in reinforced concrete often fail to account for the influence of key material parameters. To address this limitation, this study develops a new prediction model for Ccr that incorporates the water-to-binder ratio (RW/B), fly ash content (RFA), and slag content (RSG). Firstly, an accelerated experiment was conducted to determine the Ccr for 38 concrete mixtures with varying RW/B, RFA, and RSG. The experimental data were then used to quantitatively analyze the influence of these parameters on Ccr. Based on this analysis, a new multivariate nonlinear regression model was developed to predict Ccr as a function of RW/B, RFA, and RSG. The accuracy and applicability of the proposed model were validated by comparing its predictions with both the experimental data and the results from traditional models. The results indicate that Ccr decreases as RW/B, RFA, or RSG increases. This study proposes a nonlinear multiple regression model capable of predicting the Ccr in ordinary Portland cement (OPC), fly ash, slag, and blended cement concretes. The model achieved a mean absolute percentage error of 18% and a root mean square error of 0.13, demonstrating excellent accuracy and adaptability. Consequently, it provides a theoretical foundation for the durability analysis and service life assessment of marine concrete structures.