NDLI: Application of genetic programming (GP) and ANFIS for strength enhancement modeling of CFRP-retrofitted concrete cylinders

Content Provider	Springer Nature Link
Author	Jalal, Mostafa Ramezanianpour, Ali A. Pouladkhan, Ali R. Tedro, Payman
Copyright Year	2012
Abstract	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.
Starting Page	455
Ending Page	470
Page Count	16
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	23
Issue Number	2
e-ISSN	14333058
Language	English
Publisher	Springer London
Publisher Date	2012-05-05
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Soft computing Genetic programming (GP) ANFIS Artificial neural network (ANN) Concrete cylinder CFRP composites Artificial Intelligence (incl. Robotics) Data Mining and Knowledge Discovery Probability and Statistics in Computer Science Computational Science and Engineering Image Processing and Computer Vision Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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