Estimations the Combined Flexural-Torsional Strength for Prestressed Concrete Beams Using Artificial Neural Networks

Zayan, Hend S.; Mahmoud, Akram S.

doi:10.1007/978-981-16-6277-5_47

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…Alagundi and Palanisamy [24] proposed a model of ANN for prediction of shear strength of an exterior beam-column joint. Zayan and Mahmoud [25] stated that the proposed artificial neural network can successfully evaluate the combined flexural torsional strength of prestressed concrete beams.…”

Section: Introductionmentioning

confidence: 99%

Estimation of the Effective Moment of Inertia for Hybrid Concrete Beams Reinforced with Steel and FRP Bars

et al. 2023

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The application of fiber-reinforced polymer bars is rapidly rising in concrete structures because of corrosion resistance and high tensile strength. By contrast, concrete structures reinforced with Fiber-Reinforced Polymer (FRP) bars illustrate less ductility and brittle failure without warning than reinforced concrete structures with conventional steel bars. Hybrid concrete structures with the combination of FRP and steel bars can simple beams to calculate the effective moment of inertia for hybrid reinforced concrete beams. The result shows that both soft computing models are highly precise compared to experimental data.

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Section: Introductionmentioning

confidence: 99%

Estimation of the Effective Moment of Inertia for Hybrid Concrete Beams Reinforced with Steel and FRP Bars

et al. 2023

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Shear transfer strength estimation of concrete elements using generalized artificial neural network models

Zayan

Mahmoud

Hamdullah

2023

Journal of the Mechanical Behavior of Materials

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Based on published test findings, this article outlines the use of artificial neural networks (ANNs) to forecast the efficiency factor of shear transfer strength in concrete. Backpropagation neural networks with feed-forward have been employed. The ANN model was created by incorporating a huge experimental database and carefully selecting the architecture and training procedure. The presented ANN model offered a more accurate tool to compute R (where R is a measure of the closeness of association of the points in a scatterplot to a linear regression line based on those points) and capture the impacts of five primary parameters: concrete compressive strength, steel reinforcement ratio, steel yield strength, fiber volumetric ration, and steel fiber aspect ratio are given from experimental data. The obtained results reveal that the first important parameter is concrete compressive strength. In addition, ρ y f y parameter represents the normalized tensile force in steel reinforcements of section, whereas the smallest importance parameter L/D is aspect ratio of steel fibers. Also, the current study illustrated the facilities of using generalized artificial neural networks on predicting the shear transfer strength across the concrete sections, whether they are fibrous or not. From the results, the correlation factor (R 2) is estimated to be about 83%, which means it had a good correlation within the input parameters. In addition, the mean absolute percentage error was 2.06.

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Estimations the Combined Flexural-Torsional Strength for Prestressed Concrete Beams Using Artificial Neural Networks

Cited by 2 publications

References 24 publications

Estimation of the Effective Moment of Inertia for Hybrid Concrete Beams Reinforced with Steel and FRP Bars

Estimation of the Effective Moment of Inertia for Hybrid Concrete Beams Reinforced with Steel and FRP Bars

Shear transfer strength estimation of concrete elements using generalized artificial neural network models

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