The surface chloride concentration of concrete is a critical factor in determining the service life of concrete in tidal environments. This study aims to identify an effective Machine Learning (ML) model for predictin...The surface chloride concentration of concrete is a critical factor in determining the service life of concrete in tidal environments. This study aims to identify an effective Machine Learning (ML) model for predicting and assessing surface chloride concentration in such conditions. Using a database that includes 12 input variables and 386 samples of surface chloride concentration in seawater-exposed concrete, the study evaluates the predictive performance of nine ML models. Among these models, the Gradient Boosting (GB) model, using default hyperparameters, demonstrates the best performance, achieving a coefficient of determination (R2) of 0.920 and a root mean square error of 0.103% by weight of concrete for the testing data set. Furthermore, an Excel file based on the GB model is created to estimate surface chloride concentration, simplifying the mix design process according to concrete durability requirements. The Shapley additive explanation values and partial dependence plot one dimension offer a detailed analysis of the impact of the 12 variables on surface chloride concentration. The four most influential factors are, in descending order, fine aggregate content, exposure time, annual mean temperature, and coarse aggregate content. Specifically, surface chloride concentration increases linearly with prolonged exposure time, stabilizing after a certain period, while higher fine aggregate content leads to a reduction in surface chloride concentration.展开更多
文摘The surface chloride concentration of concrete is a critical factor in determining the service life of concrete in tidal environments. This study aims to identify an effective Machine Learning (ML) model for predicting and assessing surface chloride concentration in such conditions. Using a database that includes 12 input variables and 386 samples of surface chloride concentration in seawater-exposed concrete, the study evaluates the predictive performance of nine ML models. Among these models, the Gradient Boosting (GB) model, using default hyperparameters, demonstrates the best performance, achieving a coefficient of determination (R2) of 0.920 and a root mean square error of 0.103% by weight of concrete for the testing data set. Furthermore, an Excel file based on the GB model is created to estimate surface chloride concentration, simplifying the mix design process according to concrete durability requirements. The Shapley additive explanation values and partial dependence plot one dimension offer a detailed analysis of the impact of the 12 variables on surface chloride concentration. The four most influential factors are, in descending order, fine aggregate content, exposure time, annual mean temperature, and coarse aggregate content. Specifically, surface chloride concentration increases linearly with prolonged exposure time, stabilizing after a certain period, while higher fine aggregate content leads to a reduction in surface chloride concentration.
