Quantitatively characterizing sandy soil structure altered by MICP using multi-level thresholding segmentation algorithm

The influences of biological, chemical, and flow processes on soil structure through microbially induced carbonate precipitation (MICP) are not yet fully understood. In this study, we use a multi-level thresholding segmentation algorithm, genetic algorithm (GA) enhanced Kapur entropy (KE) (GAE-KE), to accomplish quantitative characterization of sandy soil structure altered by MICP cementation. A sandy soil sample was treated using MICP method and scanned by the synchrotron radiation (SR) micro-CT with a resolution of 6.5 μm. After validation, tri-level thresholding segmentation using GAE-KE successfully separated the precipitated calcium carbonate crystals from sand particles and pores. The spatial distributions of porosity, pore structure parameters, and flow characteristics were calculated for quantitative characterization. The results offer pore-scale insights into the MICP treatment effect, and the quantitative understanding confirms the feasibility of the GAE-KE multi-level thresholding segmentation algorithm.