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Modeling vibration and crack behavior of reinforced concrete beams: developing artificial neural network predictive models

Modeling vibration and crack behavior of reinforced concrete beams: developing artificial neural... A numerical method is presented for modeling the crack in the opening mode, and the vibration of cracked RC beams with composite sheets is analyzed with the aid of FEM. Through the modification of the second surface moment in two complete and cracked sections are examined in the Euler–Bernoulli beam analysis. Using continuity conditions at the crack location, the equations of two microelements of the cracked element are related, and a torsion spring simulates the crack in this study. Several factors determine stiffness, including reinforcement place, where composite sheet is placed, and how deep the crack is. As a result of applying composite sheets, steel reinforcements, and cracks to the equations, the stiffness and mass matrices are modified. Vibration analysis is implemented using these improved matrices to determine the natural frequency (NF) of beams. To ensure the results are correct and accurate, Abaqus performs a comprehensive analysis. According to the presented method, reinforced concrete structures that are crack-resistant can be analyzed using the obtained results. A machine learning algorithm is proposed to predict NF, and the results were very promising. The model had a mean absolute error (MAE) of 0.91%. The model uses hyperparameter tuning to optimize the specifications of the artificial neural network. A comparison of the computational costs of the modeling and predictive model is presented. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Asian Journal of Civil Engineering Springer Journals

Modeling vibration and crack behavior of reinforced concrete beams: developing artificial neural network predictive models

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References (45)

Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ISSN
1563-0854
eISSN
2522-011X
DOI
10.1007/s42107-023-00763-6
Publisher site
See Article on Publisher Site

Abstract

A numerical method is presented for modeling the crack in the opening mode, and the vibration of cracked RC beams with composite sheets is analyzed with the aid of FEM. Through the modification of the second surface moment in two complete and cracked sections are examined in the Euler–Bernoulli beam analysis. Using continuity conditions at the crack location, the equations of two microelements of the cracked element are related, and a torsion spring simulates the crack in this study. Several factors determine stiffness, including reinforcement place, where composite sheet is placed, and how deep the crack is. As a result of applying composite sheets, steel reinforcements, and cracks to the equations, the stiffness and mass matrices are modified. Vibration analysis is implemented using these improved matrices to determine the natural frequency (NF) of beams. To ensure the results are correct and accurate, Abaqus performs a comprehensive analysis. According to the presented method, reinforced concrete structures that are crack-resistant can be analyzed using the obtained results. A machine learning algorithm is proposed to predict NF, and the results were very promising. The model had a mean absolute error (MAE) of 0.91%. The model uses hyperparameter tuning to optimize the specifications of the artificial neural network. A comparison of the computational costs of the modeling and predictive model is presented.

Journal

Asian Journal of Civil EngineeringSpringer Journals

Published: Jan 1, 2024

Keywords: Reinforced concrete beams; Crack modeling; Artificial neural network; Composite sheets; Stress intensity factor; Machine learning; Vibration analysis

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