Predicting Shear Strength in FRP-Reinforced Concrete Beams Using Bat Algorithm-Based Artificial Neural Network

Nikoo, Mohammad and Aminnejad, Babak and Lork, Alireza and Caggiano, Antonio (2021) Predicting Shear Strength in FRP-Reinforced Concrete Beams Using Bat Algorithm-Based Artificial Neural Network. Advances in Materials Science and Engineering, 2021. pp. 1-13. ISSN 1687-8434

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Abstract

In this article, 140 samples with different characteristics were collected from the literature. The Feed Forward network is used in this research. The parameters f’c (MPa), ρf (%), Ef (GPa), a/d, bw (mm), d (mm), and VMA are selected as inputs to determine the shear strength in FRP-reinforced concrete beams. The structure of the artificial neural network (ANN) is also optimized using the bat algorithm. ANN is also compared to the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm. Finally, Nehdi et al.’s model, ACI-440, and BISE-99 equations were used to evaluate the models’ accuracy. The results confirm that the bat algorithm-optimized ANN is more capable, flexible, and provides superior precision than the other three models in determining the shear strength of the FRP-reinforced concrete beams.

Item Type: Article
Subjects: Euro Archives > Engineering
Depositing User: Managing Editor
Date Deposited: 15 Dec 2022 10:45
Last Modified: 11 Jun 2024 05:33
URI: http://publish7promo.com/id/eprint/521

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