Effect of Stirrups on the Contribution of Concrete Compressive Strength and Tensile Steel to the Shear Strength of RC Beams Using Artificial Neural Networks

2011 ◽  
Author(s):  
Hassan El-Chabib
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Shear Strength ◽  
Artificial Neural Networks ◽  
Rc Beams ◽  
Concrete Compressive Strength ◽  
Artificial Neural

Shear strength predicting of FRP-strengthened RC beams by using artificial neural networks

2014 ◽  
Vol 21 (2) ◽  
pp. 239-255 ◽  
Author(s):  
Gunnur Yavuz ◽  
Musa Hakan Arslan ◽  
Omer Kaan Baykan
Keyword(s):  
Neural Networks ◽  
Shear Strength ◽  
Artificial Neural Networks ◽  
Back Propagation ◽  
Fiber Reinforced Polymers ◽  
Rc Beams ◽  
Ann Model ◽  
Ultimate Shear Strength ◽  
Existing Building ◽  
Artificial Neural

AbstractIn this study, the efficiency of artificial neural networks (ANN) in predicting the shear strength of reinforced concrete (RC) beams, strengthened by means of externally bonded fiber-reinforced polymers (FRP), is explored. Experimental data of 96 rectangular RC beams from an existing database in the literature were used to develop the ANN model. Eight different input parameters affecting the shear strength were selected for creating the ANN structure. Each parameter was arranged in an input vector and a corresponding output vector that includes the shear strength of the RC beam. For all outputs, the ANN model was trained and tested using a three-layered back-propagation method. The initial performance of back-propagation was evaluated and discussed. In addition, the accuracy of well-known building codes in predicting the shear strength of FRP-strengthened RC beams was also examined, in a comparable way, using same test data. The study shows that the ANN model gives reasonable predictions of the ultimate shear strength of RC beams (R2≈0.93). Moreover, the study concludes that the ANN model predicts the shear strength of FRP-strengthened RC beams better than existing building code approaches.

scholarly journals Artificial neural networks application to predict bond steel-concrete in pull-out tests

2017 ◽  
Vol 10 (5) ◽  
pp. 1051-1074 ◽  
Author(s):  
A. LORENZI ◽  
B. V. SILVA ◽  
M. P. BARBOSA ◽  
L. C. P. SILVA FILHO
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Bond Stress ◽  
Concrete Compressive Strength ◽  
Pull Out ◽  
Nonlinear Approach ◽  
The Neural Network ◽  
Artificial Neural ◽  
Bond Tests

Abstract This study aims the possibility of using the pull-out test results - bond tests steel-concrete, that has been successfully carried out by the research group APULOT since 2008 [1]. This research demonstrates that the correlation between bond stress and concrete compressive strength allows estimate concrete compressive strength. However to obtain adequate answers testing of bond steel-concrete is necessary to control the settings test. This paper aims to correlate the results of bond tests of type pull-out with its variables by using Artificial Neural Networks (ANN). Though an ANN is possible to correlate the known input data (age rupture, anchorage length, covering and compressive strength of concrete) with control parameters (bond stress steel-concrete). To generate the model it is necessary to train the neural network using a database with known input and output parameters. This allows estimating the correlation between the neurons in each layer. This paper shows the modeling of an ANN capable of performing a nonlinear approach to estimate the concrete compressive strength using the results of steel-concrete bond tests.

scholarly journals APPLICATION OF ARTIFICIAL NEURAL NETWORKS TO DETERMINE CONCRETE COMPRESSIVE STRENGTH BASED ON NON‐DESTRUCTIVE TESTS

2005 ◽  
Vol 11 (1) ◽  
pp. 23-32 ◽  
Author(s):  
Jerzy Hoła ◽  
Krzysztof Schabowicz
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Concrete Compressive Strength ◽  
Data Set ◽  
Strength Based ◽  
Compressive Strength Of Concrete ◽  
Artificial Neural ◽  
Non Destructive

The paper deals with the neural identification of the compressive strength of concrete on the basis of non‐destructively determined parameters. Basic information on artificial neural networks and the types of artificial neural networks most suitable for the analysis of experimental results are given. A set of experimental data for the training and testing of neural networks is described. The data set covers a concrete compressive strength ranging from 24 to 105 MPa. The methodology of the neural identification of compressive strength is presented. Results of such identification are reported. The results show that artificial neural networks are highly suitable for assessing the compressive strength of concrete. The neural identification of the compressive strength of concrete has been verified in situ.

scholarly journals Use of Nondestructive Testing of Ultrasound and Artificial Neural Networks to Estimate Compressive Strength of Concrete

Buildings ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 44
Author(s):  
Fernando A. N. Silva ◽  
João M. P. Q. Delgado ◽  
Rosely S. Cavalcanti ◽  
António C. Azevedo ◽  
Ana S. Guimarães ◽  
...  
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Cross Sections ◽  
Influential Factors ◽  
Average Error ◽  
Ann Model ◽  
Cement Ratio ◽  
Water Cement Ratio ◽  
Artificial Neural

The work presents the results of an experimental campaign carried out on concrete elements in order to investigate the potential of using artificial neural networks (ANNs) to estimate the compressive strength based on relevant parameters, such as the water–cement ratio, aggregate–cement ratio, age of testing, and percentage cement/metakaolin ratios (5% and 10%). We prepared 162 cylindrical concrete specimens with dimensions of 10 cm in diameter and 20 cm in height and 27 prismatic specimens with cross sections measuring 25 and 50 cm in length, with 9 different concrete mixture proportions. A longitudinal transducer with a frequency of 54 kHz was used to measure the ultrasonic velocities. An ANN model was developed, different ANN configurations were tested and compared to identify the best ANN model. Using this model, it was possible to assess the contribution of each input variable to the compressive strength of the tested concretes. The results indicate an excellent performance of the ANN model developed to predict compressive strength from the input parameters studied, with an average error less than 5%. Together, the water–cement ratio and the percentage of metakaolin were shown to be the most influential factors for the compressive strength value predicted by the developed ANN model.

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