Application of genetic programming (GP) and ANFIS for strength enhancement modeling of CFRP-retrofitted concrete cylinders
Soft computing modeling of strength enhancement of concrete cylinders retrofitted by carbon-fiber-reinforced polymer (CFRP) composites using adaptive neuro-fuzzy inference system (ANFIS) and genetic programming has been carried out in the present work. A comparative study has also been presented using artificial neural network, multiple regression and some existing empirical models. The proposed models are based on experimental results collected from literature. The models represent the ultimate strength of concrete cylinders after CFRP confinement that 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 results obtained from different models are presented and compared among which the ANFIS models are considered to be the most accurate so far and quite satisfactory as compared to the experimental results.
genetic programming (gp) anfis concrete cylinder soft computing artificial neural network (ann) cfrp composites