000			a
999			_c14613 _d14613
003			OSt
005			20210317124410.0
008			210309b xxu||||| |||| 00| 0 eng d
040			_aAIKTC-KRRC _cAIKTC-KRRC
100			_913632 _aJayalekshmi, S
245			_aEmpirical Approach for Determining Axial Strength of Circular Concrete Filled Steel Tubular Columns
250			_aVol,99(2), June
260			_aKolkata _bSpringer _c2018
300			_a257-268p.
520			_a concrete filled steel tubular (CFST) columns are highly regarded in recent years as an interesting option in the construction field by designers and structural engineers, due to their exquisite structural performance, with enhanced load bearing capacity and energy absorption capacity. This study presents a new approach to simulate the capacity of circular CFST columns under axial loading condition, using a large database of experimental results by applying artificial neural network (ANN). A well trained network is established and is used to simulate the axial capacity of CFST columns. The validation and testing of the ANN is carried out. The current study is focused on proposing a simplified equation that can predict the ultimate strength of the axially loaded columns with high level of accuracy. The predicted results are compared with five existing analytical models which estimate the strength of the CFST column. The ANN-based equation has good prediction with experimental data, when compared with the analytical models.
650		0	_94621 _aCivil Engineering
700			_913739 _aSankar,Jegadesh J. S.
773	0		_tJournal of the institution of engineers (India): Series A _dSwitzerland Springer _x 2250-2149
856			_uhttps://link.springer.com/article/10.1007/s40030-018-0291- _yClick Here
942			_2ddc _cAR