Predicting the shear strength of reinforced concrete beams using artificial neural networks

Stiffness is the main parameter of the beam’s resistance to deformation. Based on advanced research, the stiffness of bamboo-reinforced concrete beams (BRC) tends to be lower than the stiffness of steel-reinforced concrete beams (SRC). However, the advantage of bamboo-reinforced concrete beams has enough good ductility according to the fundamental properties of bamboo, which have high tensile strength and high elastic properties. This study aims to predict and validate the stiffness of bamboo-reinforced concrete beams from the experimental results data using artificial neural networks (ANNs). The number of beam test specimens were 25 pieces with a size of 75 mm × 150 mm × 1100 mm. The testing method uses the four-point method with simple support. The results of the analysis showed the similarity between the stiffness of the beam’s experimental results with the artificial neural network (ANN) analysis results. The similarity rate of the two analyses is around 99% and the percentage of errors is not more than 1%, both for bamboo-reinforced concrete beams (BRC) and steel-reinforced concrete beams (SRC).

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Application of artificial neural networks to predict the deflections of reinforced concrete beams

Studia Geotechnica et Mechanica ◽

10.1515/sgem-2016-0017 ◽

2016 ◽

Vol 38 (2) ◽

pp. 37-46 ◽

Cited By ~ 4

Author(s):

Mateusz Kaczmarek ◽

Agnieszka Szymańska

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Network ◽

Reinforced Concrete ◽

Concrete Beams ◽

Reinforced Concrete Beams ◽

The Neural Network ◽

Eurocode 2 ◽

Artificial Neural ◽

Concrete Elements

Abstract Nonlinear structural mechanics should be taken into account in the practical design of reinforced concrete structures. Cracking is one of the major sources of nonlinearity. Description of deflection of reinforced concrete elements is a computational problem, mainly because of the difficulties in modelling the nonlinear stress-strain relationship of concrete and steel. In design practise, in accordance with technical rules (e.g., Eurocode 2), a simplified approach for reinforced concrete is used, but the results of simplified calculations differ from the results of experimental studies. Artificial neural network is a versatile modelling tool capable of making predictions of values that are difficult to obtain in numerical analysis. This paper describes the creation and operation of a neural network for making predictions of deflections of reinforced concrete beams at different load levels. In order to obtain a database of results, that is necessary for training and testing the neural network, a research on measurement of deflections in reinforced concrete beams was conducted by the authors in the Certified Research Laboratory of the Building Engineering Institute at Wrocław University of Science and Technology. The use of artificial neural networks is an innovation and an alternative to traditional methods of solving the problem of calculating the deflections of reinforced concrete elements. The results show the effectiveness of using artificial neural network for predicting the deflection of reinforced concrete beams, compared with the results of calculations conducted in accordance with Eurocode 2. The neural network model presented in this paper can acquire new data and be used for further analysis, with availability of more research results.

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Prediction of shear strength of FRP-reinforced concrete flexural members without stirrups using artificial neural networks

Engineering Structures ◽

10.1016/j.engstruct.2014.01.001 ◽

2014 ◽

Vol 61 ◽

pp. 99-112 ◽

Cited By ~ 34

Author(s):

S. Lee ◽

C. Lee

Keyword(s):

Neural Networks ◽

Shear Strength ◽

Artificial Neural Networks ◽

Reinforced Concrete ◽

Flexural Members ◽

Artificial Neural

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

Advances in Materials Science and Engineering ◽

10.1155/2021/5899356 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Mohammad Nikoo ◽

Babak Aminnejad ◽

Alireza Lork

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Shear Strength ◽

Reinforced Concrete ◽

Concrete Beams ◽

Bat Algorithm ◽

Pso Algorithm ◽

Reinforced Concrete Beams ◽

Feed Forward Network ◽

Artificial Neural

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.

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Predicting Shear Capacity of FRP-Reinforced Concrete Beams without Stirrups by Artificial Neural Networks, Gene Expression Programming, and Regression Analysis

Advances in Civil Engineering ◽

10.1155/2018/5157824 ◽

2018 ◽

Vol 2018 ◽

pp. 1-16 ◽

Cited By ~ 4

Author(s):

Ghazi Bahroz Jumaa ◽

Ali Ramadhan Yousif

Keyword(s):

Gene Expression ◽

Neural Networks ◽

Regression Analysis ◽

Artificial Neural Networks ◽

Reinforced Concrete ◽

Prediction Models ◽

Concrete Beams ◽

Gene Expression Programming ◽

Shear Capacity ◽

Reinforced Concrete Beams

The shear strength prediction of fiber-reinforced polymer- (FRP-) reinforced concrete beams is one of the most complicated issues in structural engineering applications. Developing accurate and reliable prediction models is necessary and cost saving. This paper proposes three new prediction models, utilizing artificial neural networks (ANNs) and gene expression programming (GEP), as a recently developed artificial intelligent techniques, and nonlinear regression analysis (NLR) as a conventional technique. For this purpose, a large database including 269 shear test results of FRP-reinforced concrete members was collected from the literature. The performance of the proposed models is compared with a large number of available codes and previously proposed equations. The comparative statistical analysis confirmed that the ANNs, GEP, and NLR models, in sequence, showed excellent performance, great efficiency, and high level of accuracy over all other existing models. The ANNs model, and to a lower level the GEP model, showed the superiority in accuracy and efficiency, while the NLR model showed that it is simple, rational, and yet accurate. Additionally, the parametric study indicated that the ANNs model defines accurately the interaction of all parameters on shear capacity prediction and have a great ability to predict the actual response of each parameter in spite of its complexity and fluctuation nature.

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