Calibration of advanced constitutive model using optimization techniques
Journal
Journal of Testing and Evaluation
Journal Volume
48
Journal Issue
3
Date Issued
2020
Author(s)
Abstract
Abstract Calibrating an advanced constitutive model is not a trivial task. Conventionally, a linear regression is used to calibrate a constitutive model using laboratory data; however, it is not sufficient to identify model parameters. Optimization methods can be used to calibrate the parameters of an advanced constitutive model. The optimization methods, including the DIRECT optimization algorithm, trust-region-reflective least squares, and genetic algorithms, are adopted in this article. The objective function is defined as the sum of the squared distances between laboratory data and model outputs with an appropriate weighting factor for suitable scaling. The use of the three optimization methods is illustrated by calibrating a modified fuzzy set plasticity model using laboratory data.
SDGs
Type
journal article