The accurate calibration of snow parameters is necessary to establish an accurate simulation model of snow, which is generally used to study tire-snow interaction. In this paper, an innovative parameter inversion method based on in situ test results is proposed to calibrate the snow parameters, which avoids the damage to the mechanical properties of snow when making test samples using traditional test methods. A coupled Eulerian-Lagrangian (CEL) model of plate loading in snow was established; the sensitivity of snow parameters to the macroscopic load-sinkage relationship was studied; a plate-loading experiment was carried out; and the parameters of snow at the experimental site were inverted. The parameter inversion results from the snow model were verified by the experimental test results of different snow depths and different plate sizes. The results show the following: (1) The material cohesive, angle of friction, and hardening law of snow have great influence on the load-sinkage relationship of snow, the elastic modulus has a great influence on the unloading/reloading stiffness of snow, and the influence of density and Poisson's ratio on the load-sinkage relationship can be ignored. (2) The correlation coefficient between the inversion result and the matching test data is 0.979, which is 0.304 higher than that of the initial inversion curve. (3) The load-sinkage relationship of snow with different snow depths and plate diameters was simulated by using the model parameter of inversion, and the results were compared with the experimental results. The minimum correlation coefficient was 0.87, indicating that the snow parameter inversion method in this paper can calibrate the snow parameters of the test site accurately.

Accurate numerical analysis in geotechnical engineering heavily relies on the constitutive model and its parameters. The advanced constitutive model can describe the complex mechanical behaviors of soil that may involve a number of parameters. However, determining the values of constitutive parameters always relies on manual trial-and-error, which can be a time-consuming process and not conducive to widespread application. This paper presents an identification method that combines machine learning with search algorithm based on the laboratory and in-situ testing. Initially, the sensitivity of constitutive parameters was analyzed by investigating the effects of variations in soil overconsolidation and structural parameters on the results of triaxial and pressuremeter tests. Subsequently, the initial state parameter values and material control parameter ranges of the soil can be identified from the triaxial tests, this is achieved by using the neural network model. In order to accurately determine the parameters value, the numerical model was established based on in-situ pressuremeter test, and traversal algorithm was implemented to search for the optimal fit values within the range of material control parameters. Finally, the proposed identification method was applied to layers 3 - 5 of Shanghai clay, and the inverted parameters exhibited a good fit with the outcomes of triaxial tests and pressuremeter tests. The combination of laboratory and in-situ testing can enhance the reliability of obtaining constitutive parameters, and this method provides an insight into the parameters identification for advanced constitutive models.

Laboratory one-dimensional consolidation tests were conducted to measure the variation trend of the soil pore pressure at the drainage boundary with time under different magnitudes of loads. Based on the test data, continuous drainage boundary interface parameters under arbitrary loads were inversely derived, the reasonableness of which was verified by comparing the theoretical values of the boundary pore pressure with the experimental results. Moreover, the one-dimensional consolidation model of the layered foundation was established with a continuous drainage boundary. The semianalytical solution of the corresponding model under an arbitrary load was given by using the boundary transformation method. A comparison with degraded results and the finite-element calculation results verified the correctness of the present solutions. Finally, the influences of the interface parameters and loading rate on the soil consolidation behavior were studied, where three different types of loads (i.e., linear, exponential, and simple harmonic) were considered. The results revealed that the consolidation rate reaches the peak value for the linear loading pattern when the loading is completed. Moreover, the exponential load used to describe the surcharge preloading method also positively influenced the theoretical analysis due to its concise expression form. When the simple harmonic load was applied, the excess pore-water pressure in the soil element presented stable periodic vibration after the first cyclic load. In addition, the loading rate and interface parameters exhibited different influences on the consolidation behaviors. The research results of this paper can provide a theoretical reference for the settlement calculation of subgrades during the construction and operation phases.