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Pore-scale-based prediction of the hygric properties of porous building materials is on the rise as an attractive alternative for the current experimental procedure. Pore-scale simulations do however require a complete pore network model for the building material. With the currently available characterization techniques, such complete pore network model cannot be established, instead typically fragmented direct (pores sizes, shapes, positions, connections, …) or indirect (pore size distribution, pore surface area, …) information is obtained. The aim of this paper is to present stochastic pore network generation, wherein the fragmented pore structure information is used to generate a complete pore network for the building material involved. The novelty of our approach lies in the generation of a PNM by matching the distributions of direct parameters as well as indirect parameters of the input data and the model. Additionally, the position of the pores are no longer bound to a cubic lattice. This workflow will first be tested on a single scale material with a relatively straightforward pore space such as sintered glass. Finally, the hygric properties of the generated network will be compared to the measured properties of the real material as a validation step.

Towards stochastic generation of 3D pore network models of building materials