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 Prediction of Compressive Strength of Concrete in Wet-Dry Environment by BP Artificial Neural Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Chengyao Liang ◽  
Chunxiang Qian ◽  
Huaicheng Chen ◽  
Wence Kang
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Statistical Analysis ◽  
Influencing Factors ◽  
Mechanical Performance ◽  
Tidal Zone ◽  
Structure Degradation ◽  
Compressive Strength Of Concrete ◽  
Artificial Neural

Engineering structure degradation in the marine environment, especially the tidal zone and splash zone, is serious. The compressive strength of concrete exposed to the wet-dry cycle is investigated in this study. Several significant influencing factors of compressive strength of concrete in the wet-dry environment are selected. Then, the database of compressive strength influencing factors is established from vast literature after a statistical analysis of those data. Backpropagation artificial neural networks (BP-ANNs) are applied to establish a multifactorial model to predict the compressive strength of concrete in the wet-dry exposure environment. Furthermore, experiments are done to verify the generalization of the BP-ANN model. This model turns out to give a high accuracy and statistical analysis to confirm some rules in marine concrete mix and exposure. In general, this model is practical to predict the concrete mechanical performance.

scholarly journals Prediction of modulus of elasticity and compressive strength of concrete specimens by means of artificial neural networks

2016 ◽  
Vol 38 (1) ◽  
pp. 65 ◽  
Author(s):  
José Fernando Moretti ◽  
Carlos Roberto Minussi ◽  
Jorge Luis Akasaki ◽  
Cesar Fabiano Fioriti ◽  
José Luis Pinheiro Melges ◽  
...  
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Modulus Of Elasticity ◽  
Compressive Strength Of Concrete ◽  
Artificial Neural

scholarly journals Prediction of Compressive Strength of Concrete Using Artificial Neural Network and Genetic Programming

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Palika Chopra ◽  
Rajendra Kumar Sharma ◽  
Maneek Kumar
Keyword(s):  
Compressive Strength ◽  
Genetic Programming ◽  
Prediction Models ◽  
Model Development ◽  
Strength Prediction ◽  
Ann Model ◽  
Concrete Compressive Strength ◽  
Compressive Strength Of Concrete ◽  
Artificial Neural

An effort has been made to develop concrete compressive strength prediction models with the help of two emerging data mining techniques, namely, Artificial Neural Networks (ANNs) and Genetic Programming (GP). The data for analysis and model development was collected at 28-, 56-, and 91-day curing periods through experiments conducted in the laboratory under standard controlled conditions. The developed models have also been tested on in situ concrete data taken from literature. A comparison of the prediction results obtained using both the models is presented and it can be inferred that the ANN model with the training function Levenberg-Marquardt (LM) for the prediction of concrete compressive strength is the best prediction tool.

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