Suffusion, one of the modes of internal erosion, occurs when fine particles are detached under hydraulic force.More fine particles are washed out with the void growth, which subsequently causes the failure of earthworks. At present, constitutive models considering suffusion are mostly established through DEM simulations and constitutive models that can capture the main features of eroded soils are quite limited. This study aims to establish constitutive equations to model the mechanical behaviour of soils subjected to suffusion by using drained triaxial experimental data. The modified subloading Cam-clay model incorporated with the normal yield surface for the eroded soil is proposed, which can express the variation of the normal yield surface with the loss of fine particles.The determination method of the erosion-related model parameters is also proposed. The erosion-related model parameters are estimated through empirical equations with curve-fitted parameters. Finally, the capability of this modified model is demonstrated through the comparisons with experimental results.

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A reliable prediction of the surface deformation of slopes is vital to better assess the fatalities and economic losses caused by landslides. Many prediction methods have been proposed to estimate the surface deformation of landslides with nonlinear characteristics. However, these methods have low accuracy and poor applicability. In this paper, a new hybrid method for surface deformation prediction was proposed, which was deduced from the Wavelet Analysis, Genetic Algorithm (GA), and Elman Algorithm. In this method, the slope surface deformation was decomposed into a trend component and a periodic component using the time series model, which were trained and predicted utilizing the GA-Elman model. The predicted slope surface deformation was the combination of the trend component and the periodic component. Then, the predicted results of slope surface deformation through GA-Elman were compared with the predicted results through Support Vector Machines (SVM), Extreme Learning Machine (ELM), Back Propagation (BP), and Genetic Algorithm-Back Propagation (GA-BP) models. The comparison was made with reference to the data retrieved from the on-site slopes and the laboratory tests. The results revealed that the proposed method highlighted reliability and could be used with higher accuracy to forecast the slope surface deformation in the process of landslides.