Permeability prediction of pervious concrete based on mix proportions and pore characteristics

Abstract

Permeability is the principal function of pervious concrete, and establishing a permeability prediction model is a prerequisite for designing and optimizing permeability. However, most of the current permeability prediction models depend on porosity or pore characteristics, which are difficult to guide the design of pervious concrete. Therefore, this study investigated the permeability of pervious concrete with different cement-aggregate ratios and gradations of aggregates, and developed prediction models for permeability coefficients based on the mix proportion parameters and pore characteristics. The results show that the two-dimensional porosity obtained from image analysis is slightly larger than the connected porosity obtained from the volumetric method. The three pore characteristics obtained from image analysis include the two-dimensional porosity, the pore number, and the equivalent pore size, all of which correlate significantly with the cement-aggregate ratio. The permeability coefficients measured by the constant and falling head methods show significant correlations with the cement-aggregate ratio and the pore characteristics and insignificant correlations with the equivalent aggregate size. Binary and ternary permeability coefficients prediction models based on the cement-aggregate ratio and the equivalent aggregate size, as well as on the two-dimensional porosity, the pore number, and the equivalent pore size, are developed, respectively. The predicted permeability coefficients are in good agreement with the experimental results, indicating that the permeability of pervious concrete can be designed and predicted according to the prediction models.