Stochastic modeling of chaotic masonry via mesostructural characterization

TL;DR

This paper compares three numerical methods for elastic homogenization of disordered masonry structures, using image analysis to derive material parameters, and evaluates their effectiveness through a case study.

Contribution

It introduces and compares perturbation, Karhunen-Loève, and Hashin-Shtrikman methods for modeling chaotic masonry based on mesostructural data.

Findings

All methods successfully incorporate image-based material parameters.

Each approach has specific advantages and limitations in modeling accuracy and computational efficiency.

The case study demonstrates practical application and comparative performance of the methods.

Abstract

The purpose of this study is to explore three numerical approaches to the elastic homogenization of disordered masonry structures with moderate meso/macro-lengthscale ratio. The methods investigated include a representative of perturbation methods, the Karhunen-Lo\`{e}ve expansion technique coupled with Monte-Carlo simulations and a solver based on the Hashin-Shtrikman variational principles. In all cases, parameters of the underlying random field of material properties are directly derived from image analysis of a real-world structure. Added value as well as limitations of individual schemes are illustrated by a case study of an irregular masonry panel.