Analytical Investigation on SCC Infilled Composite Steel Tubes Using-Ann Approach

In this research, we are going to investigate the behavior of Self Compacting Concrete Filled steel tube (CFST). Composite Circular hallow steel tubes with infill of different grades of Self Compacting Concrete are tested for ultimate load capacity. Steel tubes are compared for different lengths, cross sections, thickness and grade. Specimens were tested separately. Experimental results were compared with American Concrete Institute (ACI), Euro Code-4 (EC-4) and modeling was carried out using ANN (Artificial Neural Network) technique which is a soft tool in Matlab-R2016a. In ANN Feed forward back propagation network is used for verifying it for different hidden layers as per LM algorithm, to generate predicted ultimate load as part of static investigation. The developed ANN model has been verified with the experimental results conducted on composite steel columns. In that way, an alternative efficient method is aimed to develop for the solution of the present problem, which provides avoiding loss of time for computing some necessary parameters.