scholarly journals Neural-network-based approach to predict the deflection of plain, steel-reinforced, and bamboo-reinforced concrete beams from experimental data

2019 ◽  
Vol 1 (6) ◽  
Author(s):  
Mayank Mishra ◽  
Atul Agarwal ◽  
Damodar Maity
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Reinforced Concrete ◽  
Concrete Beams ◽  
Reinforced Concrete Beams

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

2016 ◽  
Vol 38 (2) ◽  
pp. 37-46 ◽  
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.

scholarly journals Assessing the Instantaneous Stiffness of Cracked Reinforced Concrete Beams Based on a Gradual Change in Strain Distributions

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Chunyu Fu ◽  
Dawei Tong ◽  
Yuyang Wang
Keyword(s):  
Experimental Data ◽  
Reinforced Concrete ◽  
Cross Section ◽  
Concrete Beams ◽  
Internal Force ◽  
Experimental Results ◽  
Reinforced Concrete Beams ◽  
Gradual Change ◽  
Concrete Cracking ◽  
Good Agreement

Concrete cracking causes a gradual change in strain distributions along the cross section height of reinforced concrete beams, which will finally affect their instantaneous stiffness. A method for assessing the stiffness is proposed based on the gradual change, which is considered through modeling different strain distributions for key sections in cracked regions. Internal force equilibria are adopted to find a solution to top strains and neutral axes in the models, and then the inertias of the key sections are calculated to assess the beam stiffness. The proposed method has been validated using experimental results obtained from tests on five reinforced concrete beams. The predicted stiffness and displacements are shown to provide a good agreement with experimental data. The instantaneous stiffness is proven to greatly depend on the crack number and depth. This dependence can be exactly reflected by the proposed method through simulating the gradual change in concrete strain distributions.

Crack Analysis on Reinforced Concrete Beams Strengthened by Externally Prestressing Vertical Tensioning Method

2012 ◽  
Vol 204-208 ◽  
pp. 3104-3113
Author(s):  
Bo Wen Shi ◽  
Tian Wen Wang
Keyword(s):  
Experimental Data ◽  
Reinforced Concrete ◽  
Concrete Beams ◽  
Reinforced Concrete Beams ◽  
Tension Zone ◽  
Stress Mode ◽  
Reinforced Beams ◽  
Internal Forces ◽  
Externally Prestressing ◽  
Resistance Equation

In this paper, seven reinforced concrete beams (two contrast beams and five externally reinforced beams) were tested. The crack regularity under different tension stresses was analyzed through experimental data. Taking the stress mode under pressure for the triangle area, in the tension zone in the shape of a trapezoid , the crack resistance equation of externally prestressing reinforced rectangle beam is derived from balance method of the internal forces and moment, and the results coincide with the experiment results well.

scholarly journals Limiting Reinforcement Ratios for Hybrid GFRP/Steel Reinforced Concrete Beams

2021 ◽  
Vol 11 (1) ◽  
pp. 01-11
Author(s):  
Duy Phan Nguyen ◽  
Viet Quoc Dang
Keyword(s):  
Experimental Data ◽  
Reinforced Concrete ◽  
Concrete Beams ◽  
Steel Reinforcement ◽  
Reinforcement Ratio ◽  
Reinforced Concrete Beams ◽  
Equilibrium Equations ◽  
Steel Reinforced Concrete ◽  
Glass Fiber Reinforced ◽  
Load Carrying

In this work, a theoretical approach is proposed for estimating the minimum and maximum reinforcement ratios for hybrid glass fiber reinforced polymer (GFRP)/steel-reinforced concrete beams to prevent sudden and brittle failure as well as the compression failure of concrete before the tension failure of reinforcements. Equilibrium equations were used to develop a method for determining the minimum hybrid GFRP/steel reinforcement ratio. A method for determining the maximum hybrid GFRP/steel reinforcement ratio was also developed based on the equilibrium of forces of the balanced failure mode. For estimating the load-carrying capacity of concrete beams reinforced with hybrid GFRP/steel, less than the minimum and more than the maximum reinforcement ratio is recommended. Comparisons between the proposed expressions, experimental data, and available test results in the literature shows good agreement between the theoretical and experimental data, with a maximum discrepancy of 7%.

scholarly journals Design Optimization of Reinforced Concrete Beams by Genetically Optimized Neural Network Technique

2020 ◽  
Vol 955 ◽  
pp. 012021
Author(s):  
Eshanya Tongper Nongsiej ◽  
Karthiga @ Shenbagam Natarajan ◽  
Saravanan Murugesan
Keyword(s):  
Neural Network ◽  
Reinforced Concrete ◽  
Design Optimization ◽  
Concrete Beams ◽  
Reinforced Concrete Beams
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