Macro–Mesoscopic Analysis and Parameter Calibration of Rock–Soil Strength Degradation Under Different Water Contents

Rainfall is a key triggering factor for numerous geotechnical hazards. Hence, it is necessary to investigate the degradation characteristics of rock–soil strength under different water contents. The existing macro–mesoscopic analysis methods for rock–soil strength degradation neglect the intrinsic connection between water content variations caused by external rainfall and mesoscopic mechanical mechanisms. In addition, there is a lack of discrete element method (DEM) mesoscopic parameter calibration methods for rock–soil strength under the influence of external environmental factors. Hence, this study aims to perform a macro–mesoscopic analysis and develop a parameter calibration model for the degradation of rock–soil strength under different water contents. First, the mesoscopic mechanical characteristics under different water contents are investigated by analyzing particle displacement, the bond failure rate, and the anisotropy coefficient. Interrelationships among shear strength, water content, and mesoscopic parameters are qualitatively analyzed, which indicated a macro–mesoscopic synergistic mechanism. A macro–meso-environment data set is constructed. Key mesoscopic parameters are determined using Pearson correlation (Pearson) and mutual information (MI) methods. Then, the mapping relationships are established based on ordinary least squares. The model accuracy is verified by comparing the calibrated simulation results with direct shear test results. The results show that the shear strength increases with vertical pressure under a constant water content. However, as the water content varies, the strength initially increases and then decreases. The average displacement of central particles and bond failure rate both decrease initially and then increase with rising water content, while the anisotropy coefficients show the opposite trend. Normal bond strength, tangential bond strength, and friction coefficient are determined as the key parameters. The goodness-of-fit R2 of the parameter calibration model exceeds 0.92. Among 45 validation working conditions, only two are found to have errors of 12.4% and 13.6%, and the remainder have errors below 5%.