An Innovative Scheme to Make an Initial Guess for Iterative Optimization Methods to Calibrate Material Parameters of Strain-Hardening Elastoplastic Models

Optimization can apply in almost every branch of science and technology. In particular, a gradient-based iterative method is a mathematical optimization procedure that can be used to make decisions. The gradient-based optimization method can only find a local minimum of the objective function if the algorithm starts with the appropriate initial data. The ambition of this article is to develop a new scheme to make an initial guess for iterative optimization methods to calibrate accurately the material parameters of strain-hardening elastoplastic constitutive models based on the test data. The elastoplastic models are Drucker-Prager and modified Cam-Clay, and the data obtained from triaxial, oedometric, and hydrostatic tests. The validity of proposed material parameters is evaluated using a home-made finite-element simulator. The results emphasize the ability of the proposed procedure to accurately calibrate material parameters.