Soft computing techniques for compressive strength prediction of concrete cylinders strengthened by CFRP composites

Abstract This study presents the application of soft computing techniques, namely, as multiple regressions (MRs), neural networks (NNs), genetic programming (GP), and adaptive neuro-fuzzy inference system (ANFIS) for modeling of compressive strength of carbon fiber-reinforced polymer (CFRP) confined concrete cylinders. The proposed soft computing models are based on experimental results collected from literature. They represent the ultimate strength of concrete cylinders after confinement with CFRP composites, which is in terms of diameter and height of the cylindrical specimen, ultimate circumferential strain in the CFRP jacket, elastic modulus of CFRP, unconfined concrete strength, and total thickness of CFRP layer used. The accuracy of the proposed soft computing models is very satisfactory compared to experimental results. Moreover, the results of proposed soft computing models are compared with five models existing in the literature proposed by various researchers so far and are found to be, by far, more accurate.