000			a
999			_c18886 _d18886
003			OSt
005			20230217114906.0
008			230217b xxu||||| |||| 00| 0 eng d
040			_aAIKTC-KRRC _cAIKTC-KRRC
100			_914291 _a Gopinath, Smitha
245			_aArtificial neural network based prediction for frp-confined concrete under cyclic loading
250			_aVol.103(4), Dec
260			_aUSA _bSpringer _c2022
300			_a1015-1028p.
520			_aArtificial neural network (ANN)-based model is developed for predicting the stress and strain enhancement of FRP wrapped concrete. An experimental database is considered in the investigations, which includes confinement using carbon, glass and aramid FRP on concrete. The aspect ratio, form of FRP wrap, number of confining layers, unconfined power, confining pressure and FRP characteristics are used as input parameters. Performance of proposed ANN models was evaluated by considering two indices, coefficient of determination and measure of root mean square error. The hoop strain, which is a main influencing parameter in confining pressure and dilation of FRP-confined concrete, is also predicted using ANN, for which no model predictions are available as of today. The predictive accuracy of some of the currently available models from the literature has been assessed by estimating the stress and strain enhancement due to FRP confinement by evaluating the root mean square error. The findings from the investigations show that ANN-based models can accurately predict the response close to experimental response and competitive enough compared to already existing mathematical models. The results pave way toward opening up the scope of data-driven models for the design of FRP confinement for structures.
650		0	_94642 _aHumanities and Applied Sciences
700			_920035 _aRamesh Gopal
773	0		_dSwitzerland Springer _x 2250-2149 _tJournal of the institution of engineers (India): Series A
856			_uhttps://link.springer.com/article/10.1007/s40030-022-00678-6 _yClick here
942			_2ddc _cAR