Academic Journal
Artificial neural networks prediction of in-plane and out-of-plane homogenized coefficients of hollow blocks masonry wall
| العنوان: | Artificial neural networks prediction of in-plane and out-of-plane homogenized coefficients of hollow blocks masonry wall |
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| المؤلفون: | Friaa, Houda, Laroussi Hellara, Myriam, Stefanou, Ioannis, Sab, Karam, Dogui, Abdelwaheb |
| المساهمون: | École Nationale d’Ingénieurs de Monastir (ENIM), Université de Monastir - University of Monastir (UM), Géotechnique (CERMES), Laboratoire Navier (NAVIER UMR 8205), École nationale des ponts et chaussées (ENPC)-Centre National de la Recherche Scientifique (CNRS)-Université Gustave Eiffel-École nationale des ponts et chaussées (ENPC)-Centre National de la Recherche Scientifique (CNRS)-Université Gustave Eiffel |
| المصدر: | ISSN: 0025-6455. |
| بيانات النشر: | CCSD Springer Verlag |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | Artificial neural networks (ANN), Back-propagation, Orthotropic Love-Kirchhoff plate, Hollow blocks masonry, In-plane and out-of-plane loadings, Periodic numerical homogenization, Equivalent elastic properties, Influence of bond, [SPI.MECA.MEMA]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Mechanics of materials [physics.class-ph] |
| الوصف: | International audience ; 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. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| DOI: | 10.1007/s11012-020-01134-0 |
| الاتاحة: | https://enpc.hal.science/hal-02907137 https://enpc.hal.science/hal-02907137v1/document https://enpc.hal.science/hal-02907137v1/file/Article%20PDF.pdf https://doi.org/10.1007/s11012-020-01134-0 |
| Rights: | info:eu-repo/semantics/OpenAccess |
| رقم الانضمام: | edsbas.9009852E |
| قاعدة البيانات: | BASE |
| DOI: | 10.1007/s11012-020-01134-0 |
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