Concrete Cube Testing – A Neural Network Approach, Using MATLAB 6.0

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Concrete Cube Testing – A Neural Network Approach, Using MATLAB 6.0

Concrete Cube Testing – A Neural Network Approach, Using MATLAB 6.0

Abstract

Concrete Cube Testing – A Neural Network Approach, Using MATLAB 6.0 paper is an introduction to Artificial Neural Networks. The various types of neural networks are explained and demonstrated, applications of neural networks like ANNs in ‘concrete cube test’ is described, and a detailed historical background is provided. The connection between the artificial and the real thing is also investigated and explained. Finally, the mathematical models involved are presented and demonstrated.

An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurons. This is true of ANNs as well.

Concrete Cube Testing – A Neural Network Approach, Using MATLAB 6.0

Conclusion

In this study, the ultrasonic pulse velocity test results and concrete compressive strength values were analyzed by means of multi layer feed forward back propagation neural network model. By the virtue of these results, the compressive strengths of an entirely different set of specimens were estimated using the ultrasonic pulse velocity test results and some material properties. In the analysis, gradient descent algorithm and one hidden layer was employed.