Forecast of surface chloride concentration of concrete utilizing ensemble decision tree boosted

Abstract

This study proposes the application of Ensemble Decision Tree Boosted (EDT Boosted) model for forecasting the surface chloride concentration of marine concrete C_s. A database of 386 experimental results was collected from 17 different sources covering twelve variables was used to build and verify the predictive power of the EDT model. The input factors considered the changes in eleven variables, including the contents of cement, fly ash, blast furnace slag, silica fume, superplasticizer, water, fine aggregate, coarse aggregate, annual mean temperature, chloride concentration in seawater, and exposure time. The results indicate that EDT Boosted is a good predictor of  as verified via good performance evaluation criteria, i.e., R², RMSE, MAE, MAPE values were 0.84, 0.16, 0.17, and 17%, respectively. Partial dependence plot (PDP) was then developed to correlate the eleven input variables with the . PDP implied that the strongest factor affecting Cs was the amount of fine aggregate content, chloride concentration, exposure time, amount of cement, and water, which is useful for material engineers in the design of the grade.