Neural network modeling of strength enhancement for CFRP confined concrete cylinders

This study presents the application of neural networks (NN) for the modeling of strength enhancement of CFRP (carbon fiber-reinforced plastic) confined concrete cylinders. The proposed NN model is based on experimental results collected from literature. It represents the ultimate strength of concrete cylinders after CFRP confinement which is also given in explicit form in terms of diameter, unconfined concrete strength, tensile strength CFRP laminate and total thickness of CFRP layer used. The accuracy of the proposed NN model is quite satisfactory as compared to experimental results. Moreover the results of proposed NN model are compared with 10 different theoretical models proposed by researchers so far and are found to be by far more accurate. (C) 2007 Elsevier Ltd. All rights reserved.