Improved sorptivity models for mortar and concrete based on significant process parameters

Sorptivity of cementitious composites serves as a reliable measure of durability and extensive research on the factors influencing sorptivity is available. However, the literature lacks a robust model based on statistically significant process parameters for the prediction of this important parameter. This work combines experiments designed through Taguchi's method with ANOVA to rank the effects of cement type, coarse-to-fine aggregate ratio, water-to-cement ratio, curing age, and test temperature on sorptivity. The water-to-cement ratio has been found to be the most influencing factor as is also indicated by Washburn's law. The significant parameters have been used to develop new empirical models for sorptivity of initially dry mortar and concrete. The validation results based on data obtained from literature show that the models perform robustly despite of the wide range of mix-design. The model parameters could be further refined using the delineated algorithm requiring only nine experimental runs based on a 3(2) design.

Automated summary

This study evaluates the effects of cement type, coarse-to-fine aggregate ratio, water-to-cement ratio, curing age, and test temperature on sorptivity through experiments designed using Taguchi's method and analyzed by ANOVA. The water-to-cement ratio is found to be the most influential factor and new empirical models for sorptivity are developed based on the significant parameters. The models perform robustly across a wide range of mix-design conditions.