Identification of random material properties as stochastic inversion problem

TL;DR

This paper addresses the challenge of identifying material properties of heterogeneous materials by formulating and comparing two stochastic inversion methods, one Bayesian and one based on nonlinear transformations, to better capture inherent variability.

Contribution

It introduces and compares two novel formulations of stochastic inversion for material property identification, enhancing understanding of probabilistic parameter estimation.

Findings

Bayesian approach estimates statistical moments of material parameters.

Nonlinear transformation method provides alternative probabilistic parameter estimates.

Both methods improve modeling of material heterogeneity.

Abstract

Heterogeneity of many building materials complicates numerical modelling of structural behaviour. The material randomicity can be manifested by different values of material parameters of each material specimen. To capture inherent variability of heterogeneous materials, the model parameters describing the material properties are considered as random variables and their identification consists in solving a~stochastic inversion problem. The stochastic inversion is based on searching for probabilistic description of model parameters which provides the distribution of the model response corresponding to the distribution of the observed data. The paper presents two different formulations of the stochastic inversion problem. The first formulation arises from the Bayesian inference of uncertain statistical moments of a prescribed parameters' distribution while the main idea of the second one utilizes nonlinear transformation of random model parameters from distribution of the observed data.