Estimation of Burger Model Parameters by Inverse Analysis of Oedometer Data

Abstract

Contrary to the forward analysis, inverse analysis can be effectively utilized to determine the parameters of a constitutive model commonly used to represent the stress-strain-time behavior or the loaddeformation- time behavior of the soil. This paper reports the development of a generalized inverse analysis formulation for the parameter estimation of four-parameter Burger model. The analysis is carried out by formulating the problem as that of mathematical programming in terms of identification of the design vector, the objective function and the design constraints. Thereafter, the formulated constrained nonlinear multivariable problem is solved with the aid of fmincon: an in-built constrained optimization solver module available in MatLab. In order to gain experience, a synthetic case-study is considered wherein key issues such as the determination and setting up of variable bounds, global optimality of the solution and the minimum number of data-points required for prediction of parameters are addressed. The results reveal that the developed technique is quite efficient in predicting the model parameters. The best result is obtained when the design variables are subjected to a lower bound without any upper bound. Global optimality of the solution is achieved using the developed technique. Based on the experience gained from the synthetic study, the paper reports the determination of Burger model parameters using real-time 1-D field and laboratory oedometer data.

Keywords: Inverse analysis, Burger model, constrained nonlinear multivariable problem, global optimality