Characterizing anisotropic spatial variations of uncertain mechanical parameters for clay layer using incomplete probability data

Abstract

The uncertain mechanical parameters of clay layer under torrential rain are the key to the dynamic evolution process and stability assessment of landslide geological hazards. Due to the complex environment, engineering geology and physical chemistry process, the mechanical parameters of clay layer show significant spatial variability and correlation. In addition, due to technical and economic conditions constraints, the actual investigation and test data of soft cohesive soil are very limited, which seriously restricts the stability evaluation of clay slope and the prevention of instability disaster. To characterize anisotropic spatial variations of uncertain mechanical parameters for clay layer using incomplete probability data, the elastic modulus, Poisson ratio and shear strength under saturated conditions were measured, and statistical data and variation properties of uncertain mechanical parameters were analyzed. A modeling approach was proposed for characterizing incomplete probability data of clay layer. The accuracy of the proposed approach is verified by comparison of the statistical characteristic for measured data and simulated data. A novel linear fitting method was proposed for assessing scale of fluctuation and autocorrelation distances. The variability and correlation of uncertain mechanical properties for soft cohesive soil layer are discussed. The results show that the mechanical properties of the clay layer are uncertain in spatial position. Both the original observation data and the simulated data of three mechanical parameters have symmetrical correlation structure. The clay layer display the horizontal layered structure on the soil profile, and the vertical autocorrelation distances are shorter than the horizontal distances. This paper clearly illustrates the anisotropic spatial variations of uncertain mechanical parameters for clay layer using incomplete probability data and it can provide scientific data for the uncertainty analysis and risk assessment of clay slope under torrential rain conditions.