Load-deflection Analysis of CFRP Strengthened RC Slab Using Focused Feed-forward Time Delay Neural Network

In this paper, the load-deflection analysis of the Carbon Fiber Reinforced Polymer (CFRP) strengthened Reinforced Concrete (RC) slab using Focused Feed-forward Time Delay Neural Network (FFTDNN) is investigated. Six reinforced concrete slabs having dimension 1800×400×120 mm with similar steel bar of 2T10 and strengthened using different length and width of CFRP were tested and compared with similar samples without CFRP. The experimental load-deflection results were uploaded, normalized, and converted to a time sequence parameter in MATLAB software. Loading, time, and the effect of the different CFRP strip lengths on the slab moment of inertia were as neurons in input layer and mid-span deflection was as neuron in output layer. The network was generated using feed-forward network and a tapped delay line at the input layer to memorize the input data while training process. From 122 load-deflection data, 111 data utilized for network generation and 11 data for the network testing. The results of model on the testing stage showed that the generated FFTDNN predicted the load-deflection analysis of the slabs in acceptable technique with a correlation of determination of 0.98. The ratio between predicted deflection by FFTDNN and experimental output was in the range of 0.92 to 1.23