Parameter Identification of Soil Hyperbolic Constitutive Model by Inverse Analysis Procedure

Abstract

A tangent modulus of soil mass which allows for a piece-wise linear approximation of the hyperbolic response curve is particularly suited for incremental construction simulation. The parameter identification of nonlinear constitutive model of soil mass is based on an inverse analysis procedure, which consists of minimizing the objective function representing the difference between the experimental data and the calculated data of the mechanical model. The artificial neural network is applied to estimate the model parameters of soil mass. The weights of neural network are trained by using the Levenberg-Marquardt approximation which has a fast convergent ability. The parameter identification results illustrate that the proposed neural network has not only higher computing efficiency but also better identification accuracy. The numerically computational results with finite element method show that the forecasted displacements at observing points according to identified model parameters can precisely agree with the observed displacements.