A four-variable global–local shear deformation theory for the analysis of deep curved laminated composite beams

Lezgy-Nazargah, M.

doi:10.1007/s00707-019-02593-7

Cited by 22 publications

(6 citation statements)

References 26 publications

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“…To fill this gap, a refined shear deformation theory [51], which is based on the double superposition concept, and has only four unknown variables, is developed for vibration, buckling, and bending analyses of shallowto-deep FG curved sandwich beams. This theory not only gives accurate results but also is computationally low-cost due to the low number of unknown variables and degrees of freedom of FEM.…”

Section: Introductionmentioning

confidence: 99%

Bending, buckling and free vibration analyses of shallow-to-deep FG curved sandwich beams using a global–local refined shear deformation theory

Lezgy-Nazargah

Karamanlı

2023

Structures

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Section: Introductionmentioning

confidence: 99%

Bending, buckling and free vibration analyses of shallow-to-deep FG curved sandwich beams using a global–local refined shear deformation theory

Lezgy-Nazargah

Karamanlı

2023

Structures

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“…The flexure-shear interaction, concrete strength, the composite behavior of rebar and concrete, yield strength of reinforcement, size effect, reinforcement ratio, the ratio of effective depth, and other parameters influence the shear strength and shear behavior of deep beams. Recent research on the shear strength of RC beams resulted in the presentation of analytical and numerical methods such as the strut-and-tie model [13,14], mechanism analysis, and the bound theorem of plasticity theory [15][16][17]. Researchers developed equations for calculating of the RCD beams' shear strength [18,19].…”

Section: Introductionmentioning

confidence: 99%

Efficiency of Hybrid Algorithms for Estimating the Shear Strength of Deep Reinforced Concrete Beams

Barkhordari

Feng

Tehranizadeh

2022

Period. Polytech. Civil Eng.

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Earthquakes occurred in recent years have highlighted the need to examine the strength of reinforced concrete (RC) members. RC beams are one of the elements of reinforced concrete structures. Due to the dramatic increase in the population and the number of medium/high-rise buildings, in recent years, the beams of buildings have been mainly designed and executed in the type of deep beams. In this study, the artificial neural network (ANN) with optimization algorithms, including particle swarm optimization (PSO), Archimedes optimization algorithm (AOA), and sparrow search algorithm (SSA), are used to determine the shear strength of reinforced concrete deep (RCD) beams. 271 samples from experimental tests are employed to develop algorithms. The results of this study, design codes equations, and previous research are compared. Comparison between the results shows that the PSO-ANN algorithm is more accurate than previous methods. Finally, SHApley Additive exPlanations (SHAP) method is utilized to explain the predictions. SHAP reveals that the beam span and the ratio of the beam span to beam depth have the highest impact in predicting shear strength.

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“…In the past several decades, many methods have been proposed to analyze the shear strength of deep beams, including the strut-and-tie method (STM) [12,13] and the upper limit theorem of plasticity theory [14,15]. Based on the STM, theoretical methods to calculate the shear strength are proposed, such as compression field theory (CFT) and modified compression field theory (MCFT) [16,17], the theory of softened strut-and-tie model (SSTM) considering the compression softening of concrete [18,19], and strutand-tie model based on the crack band theory [20,21].…”

Section: Introductionmentioning

confidence: 99%

On the Training Algorithms for Artificial Neural Network in Predicting the Shear Strength of Deep Beams

Nguyen

Thi

et al. 2021

Complexity

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This study aims to predict the shear strength of reinforced concrete (RC) deep beams based on artificial neural network (ANN) using four training algorithms, namely, Levenberg–Marquardt (ANN-LM), quasi-Newton method (ANN-QN), conjugate gradient (ANN-CG), and gradient descent (ANN-GD). A database containing 106 results of RC deep beam shear strength tests is collected and used to investigate the performance of the four proposed algorithms. The ANN training phase uses 70% of data, randomly taken from the collected dataset, whereas the remaining 30% of data are used for the algorithms’ evaluation process. The ANN structure consists of an input layer with 9 neurons corresponding to 9 input parameters, a hidden layer of 10 neurons, and an output layer with 1 neuron representing the shear strength of RC deep beams. The performance evaluation of the models is performed using statistical criteria, including the correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The results show that the ANN-CG model has the best prediction performance with R = 0.992, RMSE = 14.02, MAE = 14.24, and MAPE = 6.84. The results of this study show that the ANN-CG model can accurately predict the shear strength of RC deep beams, representing a promising and useful alternative design solution for structural engineers.

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A four-variable global–local shear deformation theory for the analysis of deep curved laminated composite beams

Cited by 22 publications

References 26 publications

Bending, buckling and free vibration analyses of shallow-to-deep FG curved sandwich beams using a global–local refined shear deformation theory

Bending, buckling and free vibration analyses of shallow-to-deep FG curved sandwich beams using a global–local refined shear deformation theory

Efficiency of Hybrid Algorithms for Estimating the Shear Strength of Deep Reinforced Concrete Beams

On the Training Algorithms for Artificial Neural Network in Predicting the Shear Strength of Deep Beams

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