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Title: Soft computing-based models for the prediction of masonry compressive strength
Author: Asteris, Panagiotis G.
Lourenço, Paulo B.
Hajihassani, Mohsen
Adami, Chrissy-Elpida N.
Lemonis, Minas E.
Skentou, Athanasia D.
Marques, Rui
Nguyen, Hoang
Rodrigues, Hugo
Varum, Humberto
Keywords: Artificial neural networks
Genetic programming
Machine learning
Metaheuristic algorithms
Compressive strength
Issue Date: 1-Dec-2021
Publisher: Elsevier
Abstract: Masonry is a building material that has been used in the last 10.000 years and remains competitive today for the building industry. The compressive strength of masonry is used in modern design not only for gravitational and lateral loading, but also for quality control of materials and execution. Given the large variations of geometry of units and joint thickness, materials and building practices, it is not feasible to test all possible combinations. Many researchers tried to provide relations to estimate the compressive strength of masonry from the constituents, which remains a challenge. Similarly, modern design codes provide lower bound solutions, which have been demonstrated to be weakly correlated to observed test results in many cases. The present paper adopts soft-computing techniques to address this problem and a dataset with 401 specimens is considered. The obtained results allow to identify the most relevant parameters affecting masonry compressive strength, areas in which more experimental research is needed and expressions providing better estimates when compared to formulas existing in codes or literature.
Peer review: yes
DOI: 10.1016/j.engstruct.2021.113276
ISSN: 0141-0296
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RISCO - Artigos

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