Optimum Design of Flexural Strength and Stiffness for Reinforced Concrete Beams Using Machine Learning
نویسندگان
چکیده
In this paper, an optimization approach was presented for the flexural strength and stiffness design of reinforced concrete beams. Surrogate modeling based on machine learning applied to predict responses structural system in three-point flexure tests. Three input variables, area steel bars compression zone, tension shear were adopted dataset arranged by Box-Behnken method. The composed thirteen specimens tested under loading at age 28 days both failure load maximum deflection values recorded. Compression tests conducted obtain data analysis numerical modeling. Afterward, finite element performed all using ATENA program verify experimental Subsequently, surrogate models constructed. Finally, supporting factorial method predicted responses. proved be excellent tool optimize beams stiffness. addition, results very good agreement terms type cracking pattern.
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ژورنال
عنوان ژورنال: Applied sciences
سال: 2021
ISSN: ['2076-3417']
DOI: https://doi.org/10.3390/app11188762