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

Asteris, P., Lemonis, M., Le, T. & Tsavdaridis, K. D. ORCID: 0000-0001-8349-3979 (2021). Evaluation of the ultimate eccentric load of rectangular CFSTs using advanced neural network modeling. Engineering Structures, 248, article number 113297. doi: 10.1016/j.engstruct.2021.113297


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.

Publication Type: Article
Additional Information: © 2022. This manuscript version is made available under the CC-BY-NC-ND 4.0 license
Publisher Keywords: Concrete-Filled Steel Tube (CFST), Artificial neural networks (ANNs), Load eccentricity, Rectangular CFST, Ultimate load
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TH Building construction
Departments: School of Science & Technology > Engineering
SWORD Depositor:
[thumbnail of 220212 Marked_Manuscript (for symplectic).pdf]
Text - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

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