Evaluation of the ultimate eccentric load of rectangular CFSTs using advanced neural network modeling

• Published in: Engineering structures 2021-12, Vol.248, p.113297, Article 113297
• Main Authors: Asteris, Panagiotis G., Lemonis, Minas E., Le, Tien-Thinh, Tsavdaridis, Konstantinos Daniel
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Artificial neural networks (ANNs); Columns (structural); Concrete properties; Concrete-Filled Steel Tube (CFST); Eccentric loads; Eccentricity; Load eccentricity; Neural networks; Performance measurement; Rectangular CFST; Steel tubes; Ultimate load
• ISSN: 0141-0296; 1873-7323
• DOI: 10.1016/j.engstruct.2021.113297

•An optimization strategy was conducted to obtain a final set of ANN model.•The developed ANN model exhibited superior performance than current codes and empirical equations.•An explicit equation based on Artificial Neural Networks were derived for practical application. In this paper an Artificial Neural Network (ANN) model is developed for the prediction of the ultimate compressive load of rectangular Concrete Filled Steel Tube (CFST) columns, taking into account load eccentricity. To this end, an experimental database of CFST specimens from the literature has been compiled, totaling 1224 individual tests, both under concentric and under eccentric loading. Except for eccentricity, other parameters taken into consideration include the cross section width, height and thickness, the steel yield limit, the concrete strength and the column length. Both short and long specimens were evaluated. The architecture of the proposed ANN model was optimally selected, according to predefined performance metrics. The developed model was then compared against available design codes. It was found that its accuracy was significantly improved while maintaining a stable numerical behavior. The explicit equation that describes mathematically the ANN is offered in the paper, for easier implementation and evaluation purposes.