The inclusion of calcite precipitates (CaCO3) in soft soil can improve the mechanical properties. Understanding the variability in sand stiffness due to heterogeneous precipitates is crucial for stiffness evaluation and prediction. A novel discrete element-Monte Carlo (DE-MC) method was proposed to quantify the sand stiffness variability induced by stochastic distributions of calcite precipitates, specifically focusing on shear wave velocity (Vs) as an indicator of soil stiffness. A total of 1972 samples were constructed to simulate stochastic spatial distributions of calcite precipitates. Through joint stochastic analysis, the preferential paths formed by calcite clusters were identified as significant contributors to Vs variability. The normalized connectivity per unity distance contact weight (Cd,n) exhibited the most correlated relation with Vs. Two weight selection methods were applicable for using Cd,n to characterize and predict Vs. The results suggest that the DE-MC method has the potential to assess the variability in sand stiffness quantitatively.
