Gaussian process emulation for rapid in-plane mechanical homogenization of periodic masonry

Gaussian process emulation for rapid in-plane mechanical homogenization of periodic masonry

Luis C.M. da Silva, André Jesus, Gabriele Milani

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Abstract. Numerical homogenization strategies can provide average mechanical responses, either in stress or coupled-stress quantities, which include many phenomenological features. Nonetheless, a direct application of numerical homogenization in sensitivity analysis in which uncertainty is propagated becomes impractical, as hundreds or thousands of simulations are conventionally required. In this study, a reliable and rapid predictive surrogate model is developed aiming to characterize the homogenized response of masonry. The case of English-bond arrangement is addressed, and the following steps are considered: (1) creation of a synthetic database through numerical homogenization based on a Finite-Element method, generated by randomization of model parameters; (2) training of a nonlinear Gaussian process; and (3) approximation of homogenized stress-strain curves for a masonry wall and for both linear and nonlinear ranges. The performance of the proposed technique is evaluated using training-validation-test in terms of computational accuracy. Results indicate that computational time is lessened 1200% while relative errors remain below 5-10%.

Masonry Homogenization, Gaussian Process, Surrogate Modelling

Published online 3/17/2022, 6 pages
Copyright © 2023 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA

Citation: Luis C.M. da Silva, André Jesus, Gabriele Milani, Gaussian process emulation for rapid in-plane mechanical homogenization of periodic masonry, Materials Research Proceedings, Vol. 26, pp 325-330, 2023


The article was published as article 53 of the book Theoretical and Applied Mechanics

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

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