• Mr. Pavan Kumar S R
• Mr. Athiq Ulla Khan
• Dr. N.S Kumar

Concrete Filled Steel Tubular (CFST ) columns have significance in recent years due to superior structural aspects especially in developed countries. It is crucial to study the ultimate load carrying capacity of concrete-filled steel tubular columns to ensure the safe operation of engineering structures. Numerous studies were reported on the behaviour of the ultimate load carrying capacity of CFST columns with Self Compacting Concrete (SCC) as infill’s at ambient and elevated temperature for varies length, thickness and diameter. This study further presents an analytical model to predict a the ultimate load carrying capacities of CFST columns with SCC infill’s at ambient and elevated temperature by using ANN and PSO, which are the tools of the MTALAB-R2018b. The experimental dataset that presented in previous studies are utilized to verify the reliability of the ANN & PSO and the prediction performance of the model is compared with that of traditional design methods. The consolidated input data consisting of all ranges of variables, which are used for the development of the model. It is shown that the proposed method can predict well, the ultimate load capacity of circular section concrete filled steel tube columns are found to match with the corresponding experimental results.

CFST; SCC; Artificial Neural Network(ANN); PSO (Particle Swarm Optimization); Ambient and Elevated Temperature.

"PREDICTION OF ULTIMATE LOAD CARRYING CAPACITY OF CFST COLUMNS (SCC INFILLS) USING MATLAB AT AMBIENT AND ELEVATED TEMPERATURE", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 7, page no.64-75, July-2020, Available :http://www.jetir.org/papers/JETIR2007307.pdf

"PREDICTION OF ULTIMATE LOAD CARRYING CAPACITY OF CFST COLUMNS (SCC INFILLS) USING MATLAB AT AMBIENT AND ELEVATED TEMPERATURE", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 7, page no. pp64-75, July-2020, Available at : http://www.jetir.org/papers/JETIR2007307.pdf