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Meccanica
Erschienen in: Meccanica 3/2020

12.02.2020

Artificial neural networks prediction of in-plane and out-of-plane homogenized coefficients of hollow blocks masonry wall

verfasst von: Houda Friaa, Myriam Laroussi Hellara, Ioannis Stefanou, Karam Sab, Abdelwaheb Dogui

Erschienen in: Meccanica | Ausgabe 3/2020

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Abstract

A masonry wall is a composite structure characterized by a large variety in geometrical and material parameters. The determination of the effective macroscopic properties, through the homogenization scheme, depends on a great number of variables. Thus, in order to replace heavy numerical simulation, in this paper, the use of artificial neural networks (ANN) is proposed to predict elastic membrane and bending constants of the equivalent Love–Kirchhoff plate of hollow concrete blocks masonry wall. To model the ANN, a numerical periodic homogenization in several parameters is used. To construct the model, five main material and geometrical input parameters are utilized. Multilayer perceptron neural networks are designed and trained (with the best selected ANN model) by the sets of input–output patterns using the backpropagation algorithm. As a result, in both training and testing phases, the developed ANN indicates high accuracy and precision in predicting the equivalent plate of a hollow masonry wall with insignificant error rates compared to FEM results.
Vorheriger Artikel Discontinuum analysis of the fracture mechanism in masonry prisms and wallettes via discrete element method
Nächster Artikel A modified conjugated bond-based peridynamic analysis for impact failure of concrete gravity dam

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Metadaten
Titel
Artificial neural networks prediction of in-plane and out-of-plane homogenized coefficients of hollow blocks masonry wall
verfasst von
Houda Friaa
Myriam Laroussi Hellara
Ioannis Stefanou
Karam Sab
Abdelwaheb Dogui
Publikationsdatum
12.02.2020
Verlag
Springer Netherlands
Erschienen in
Meccanica / Ausgabe 3/2020
Print ISSN: 0025-6455
Elektronische ISSN: 1572-9648
DOI
https://doi.org/10.1007/s11012-020-01134-0

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