Spatial variability and stochastic finite element model of unreinforced masonry veneer wall system under Out-of-plane loading

Imrose B. Muhit, Mark J. Masia, Mark G. Stewart, Andrea C. Isfeld

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Inconsistency in the quality of workmanship, the weather during construction, and materials may result in the high unit-to-unit spatial variability of mechanical properties in the same masonry structure. This paper develops a numerical modelling strategy and focuses on the stochastic assessment of unreinforced masonry (URM) veneer walls with spatially variable wall constituent material properties subjected to out-of-plane loading. 3-D finite element modelling of experimentally tested veneer walls was conducted using a micro-modelling approach combined with the Monte Carlo Simulation technique. Spatial stochastic finite element analysis considered the spatial variability of the properties of the wall components (mortar flexural tensile strength) and compared them with non-spatial analysis. The non-spatial analysis overestimates the wall system failure compared to spatial analysis, and the spatial analysis is considered to more realistically represent the variabilities of the URM veneer wall system. Sensitivity analysis is conducted to check the sensitivity of the veneer system behaviour to variability in the various input parameters. The variability of experimental results obtained in a parallel study are quantified and compared with stochastic finite element analysis (SFEA) results. The SFEA model developed in this study can estimate the behaviour and system peak load reasonably and are considered to be from the similar population as test results. Model errors are also included to assess the efficiency of the SFEA model.

Original languageEnglish
Article number114674
Number of pages20
JournalEngineering Structures
Early online date17 Jul 2022
Publication statusPublished - 15 Sept 2022
Externally publishedYes

Bibliographical note

Funding Information:
The authors wish to acknowledge the financial support of the Australian Research Council under Discovery Project DP180102334.

Publisher Copyright:
© 2022 The Author(s)


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