scholarly journals The Prediction of Stiffness of Bamboo-Reinforced Concrete Beams Using Experiment Data and Artificial Neural Networks (ANNs)

Crystals ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 757 ◽  
Author(s):  
Muhtar ◽  
Amri Gunasti ◽  
Suhardi ◽  
Nursaid ◽  
Irawati ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Reinforced Concrete ◽  
Concrete Beams ◽  
Experimental Results ◽  
Reinforced Concrete Beams ◽  
Steel Reinforced Concrete ◽  
Simple Support ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

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).

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.

A neural network model for predicting maximum shear capacity of concrete beams without transverse reinforcement

2005 ◽  
Vol 32 (4) ◽  
pp. 644-657 ◽  
Author(s):  
Ayman Ahmed Seleemah
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Experimental Work ◽  
Concrete Beams ◽  
Shear Capacity ◽  
Transverse Reinforcement ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Different relationships have been proposed by codes and researchers for predicting the shear capacity of members without transverse reinforcement. In this paper, the applicability of the artificial neural network (ANN) technique as an analytical alternative to existing methods for predicting this shear capacity is investigated using a critically reviewed and agreed upon database of experimental work that serves as a basis of comparison and (or) assessment of existing and new relationships. Both ANN and eight different codes and researcher's predictions of the shear capacity of the specimens of the database were compared. The ANN predictions are much superior to those of any of the current available relationships.Key words: artificial neural networks, shear capacity, transverse reinforcement, beams.

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

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
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.

Deflections of Steel Reinforced Concrete Beams Considering the Bond-Slip Effects

2012 ◽  
Vol 226-228 ◽  
pp. 1128-1131
Author(s):  
Xiu Li Cao ◽  
Gang Ye ◽  
Hai Gang Gou
Keyword(s):  
Reinforced Concrete ◽  
Concrete Beams ◽  
Experimental Studies ◽  
Experimental Results ◽  
Reinforced Concrete Beams ◽  
Reinforced Concrete Structures ◽  
Bond Slip ◽  
Steel Reinforced Concrete ◽  
Slip Effects ◽  
Current Standards

Bond-slip performance between section steel and concrete has influence on deformation of steel reinforced concrete beams based on results of experimental studies. Current standards on steel reinforced concrete structures do not involve bond-slip effects when calculating the deflection of steel reinforced concrete beams and this is not valid exactly. This paper describes a new method of deflection calculation for steel reinforced concrete beams, which considering the bond-slip effects on deformation. Deflection of steel reinforced concrete beams are divided into two parts: deflection of steel reinforced concrete beams under loads considering the fully bond between steel and concrete, and the additional deflection caused by the bond-slip. The sum of the two parts is the total deflection. Results show that the proposed method in this paper fits with experimental results.

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