Predicting the permeability of consolidated silty clay via digital soil reconstruction

Abstract

Numerical modelling of the pore structure of porous media is still one of the most powerful tools for predicting the soil’s permeability and transport properties for geoengineers. However, that approach relies heavily on both the availability and accuracy of microstructural information, which is often challenging to acquire. In this study, statistically equivalent 3D micropore structures of soil samples were reconstructed using a digital soil model, which generated the 3D microstructures using a probabilistic map derived from 2D cross-section images and parameter sets calibrated via a deep learning neural network. The reconstructed images of the 15 micropore structures generated for each of two soil samples with different consolidation pressures (no disturbance and 3200 kPa) indicated that the pores become homogeneous with the consolidation process. The tortuosity factor was found to have a linear correlation with voxel size for simulation. The permeability of each soil sample was computed using the ABAQUS CFD module, and the results of the simulation were comparable to those for the constant head permeability test.