Development of a MATLAB-based code for quantification of effective void space in porous pavement

Abstract

Porous pavement is a well-documented, low-impact stormwater management technique. When it comes to design of the top layer, the amount of void space (porosity) is often of interest as it influences both infiltration and strength of the pavement. Laboratory equipment can be used to measure the porosity of core samples, but when more detail is required, other equipment or methods must be used. One such method is to scan the entire sample using a computer tomography (CT) machine and then perform some image processing techniques on the scanned data to reconstruct the sample digitally. While the workflow of scanning and processing to produce the 3D digital twin of porous pavement is not new and can be in fact done by open-source or commercial software, there are still some parts of the process that deserve a deeper investigation, for example binarization and segmentation algorithms applied to the solid-and-void space and void space, respectively. This is difficult to do with commercial software which operates like a black-box, and there needs to be more open-source codes that are user-friendly, extendable, and competitive to what commercial software can do. This work presents a MATLAB-based code that allows for a deeper investigation of how one can accurately and efficiently quantify the effective (or connected) void space of a porous pavement sample from a 3D digital model. We demonstrate the effect of dataset coarsening, which can be used to reduce the computational intensity of the algorithm while preserving accuracy. The code is publicly available online to allow for reproducible research and the possibility of extensions for increased functionality and complexity.