A review of materials science-based models for mixture design and permeability prediction of pervious concretes

Abstract

Pervious concrete is one of the relatively recent additions to the class of sustainable multifunctional cement-based materials. The material design of pervious concretes relies on trial-and-error-based approaches since the larger porosity and pore size requirements make a minimal porosity-based approach adopted for conventional concretes non-viable. This paper reviews a particle packing-based methodology for pervious concrete material design using a compaction index from compressible packing model of granular particles as the defining parameter. The pore structure features of the thus designed pervious concretes are characterised using well-accepted stereological and morphological methods. A three-dimensional reconstruction procedure, from two-dimensional starting images, used to develop material structures in which performance (permeability) prediction algorithms can be implemented is also reviewed. Permeability of these model structures have been predicted using a Stokes' solver and a Lattice Boltzmann scheme, and compared to the experimentally determined permeability. A stochastic Monte-Carlo simulation is used to quantify the influence of pore structure features on the permeability of pervious concretes.