Soft computing-based models for the prediction of masonry compressive strength

被引：87

作者：

Asteris, Panagiotis G. ^{[1
]}

Lourenco, Paulo B. ^{[2
]}

Hajihassani, Mohsen ^{[3
]}

Adami, Chrissy-Elpida N. ^{[1
]}

Lemonis, Minas E. ^{[1
]}

Skentou, Athanasia D. ^{[1
]}

Marques, Rui ^{[2
]}

Hoang Nguyen ^{[4
]}

Rodrigues, Hugo ^{[5
]}

Varum, Humberto ^{[6
]}

机构：

[1] Sch Pedag & Technol Educ, Computat Mech Lab, GR-14121 Athens, Greece

[2] Univ Minho, Dept Civil Engn, ISISE, Guimaraes, Portugal

[3] Urmia Univ, Fac Engn, Dept Min Engn, Orumiyeh 5756151818, Iran

[4] Hanoi Univ Min & Geol, Min Fac, Dept Surface Min, 18 Vien St, Hanoi, Vietnam

[5] Univ Aveiro, Dept Civil Engn, RISCO, Aveiro, Portugal

[6] Univ Porto, Civil Engn Dept, CONSTRUCT LESE, Fac Engn, Porto, Portugal

来源：

ENGINEERING STRUCTURES | 2021年 / 248卷

关键词：

Artificial neural networks; Genetic programming; Machine learning; Masonry; Metaheuristic algorithms; Compressive strength; STRESS-STRAIN CHARACTERISTICS; ARTIFICIAL NEURAL-NETWORK; PATTERN-CLASSIFICATION; SEISMIC VULNERABILITY; BRICK MASONRY; BOND STRENGTH; PRISMS; UNCERTAINTIES; PERFORMANCE; ELASTICITY;

D O I：

10.1016/j.engstruct.2021.113276

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

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 constit-uents, 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.

引用

页数：27

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