The application of neural network techniques to the analysis of reinforced concrete beam-column joints subjected to axial load and bi-axial bending

The application of neural networks in the form of parameter predictions to the behaviour and strength of beam-column joints under axial load and biaxial bending has been studied. Computation algorithms in the form of numerical analysis were performed on the beam-column joints to simulate the existing experimental data. A systematic approach was provided by implementing neural networks in the form of prediction by backpropagation algorithms. The objective of this study was to demonstrate a concept and methodology, rather than to build a full-scale knowledge-based system model, by incorporating most of the fundamental aspects of a neural network to solve the complex non-linear mapping of a beam-column joint. In general, it should be possible to identify certain parameters and allow the neural network to develop the model, thus accounting for the observed behaviour without relying on a particular algorithm but depending entirely on the manipulation of numerical data. The aim of this study was to view available experimental data on beam-column joint parameters from different angles and establish a concept and methodology that would provide rapid and economic benefits to experimental research. The focus of this study is to reconstruct previous experimental work by evaluating several parameters and establish valid mathematical relationships based on neural networks which are in agreement with relationships based on the experimental results. The computational methodology considered for the analysis of the beam-column joints has been formulated by adopting three stages to establish a procedure to implement the concept and methodology proposed. The procedure is demonstrated

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