Estimation and prediction of dynamic resilient modulus of Yellow River silt under long-term dynamic loading based on mathematical statistics method

Abstract Traversing the erosion-prone Loess Plateau, the Yellow River is notable for having the highest average sediment concentration globally. Given its local availability and cost-effectiveness, this silt has been commonly utilized as a construction material in the region. Nevertheless, a significant research gap remains regarding the assessment of its mechanical properties and stability. This investigation focuses on examining how stress states and physical characteristics influence the dynamic resilient modulus ( M r ) of Yellow River silt (YRS) under prolonged dynamic loading. To this end, repeated load triaxial (RLT) tests were performed, applying 10,000 loading cycles and varying key parameters including confining pressure ( σ ₃), relative density ( D r ), loading frequency ( f ), and cyclic stress ratio (CSR). Statistical methods were employed to determine the confidence intervals and distribution patterns of the M r values across these different test conditions. Results indicated that the silt exhibits cyclic hardening behavior under cyclic loading. The minimum recorded M r value exceeded 64.4 MPa across all tested scenarios. The influence of individual factors was quantified by using both power exponent and linear regression models. Furthermore, a comprehensive predictive model for estimating M r was developed, incorporating confining pressure ( σ ₃), relative density ( D r ), loading frequency ( f ), and cyclic stress ratio (CSR) through factor analysis and multivariate nonlinear regression. A comparison between measured and predicted M r values confirmed the model's applicability. These outcomes provide valuable insights into the mechanical evaluation and stability assessment of embankment structures built with YRS.