Utilization of artificial neural networks to prediction of the capacity of CCFT short columns subject to short term axial load
- 3 February 2014
- journal article
- Published by Springer Science and Business Media LLC in Archives of Civil and Mechanical Engineering
- Vol. 14 (3), 510-517
- https://doi.org/10.1016/j.acme.2014.01.006
Abstract
No abstract availableKeywords
This publication has 18 references indexed in Scilit:
- Prediction of compressive strength of concrete containing construction and demolition waste using artificial neural networksConstruction and Building Materials, 2013
- An approach for estimating the capacity of RC beams strengthened in shear with FRP reinforcements using artificial neural networksConstruction and Building Materials, 2012
- Predicting the core compressive strength of self-compacting concrete (SCC) mixtures with mineral additives using artificial neural networkConstruction and Building Materials, 2011
- Behaviour of short and slender concrete-filled stainless steel tubular columnsJournal of Constructional Steel Research, 2010
- Prediction of FRP-confined compressive strength of concrete using artificial neural networksComposite Structures, 2010
- Experimental study on steel tubular columns in-filled with plain and steel fiber reinforced concreteThin-Walled Structures, 2010
- Numerical model for the behavior and capacity of circular CFT columns, Part II: Verification and extensionEngineering Structures, 2008
- An experimental behaviour of concrete-filled steel tubular columnsJournal of Constructional Steel Research, 2005
- Axial capacity of circular concrete-filled tube columnsJournal of Constructional Steel Research, 2004
- Behaviour of concrete-filled hollow structural steel (HSS) columns with pre-load on the steel tubesJournal of Constructional Steel Research, 2003