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
Abstract
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 |
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Additional Information: | © 2022. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/ |
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: |
Available under License Creative Commons Attribution Non-commercial No Derivatives.
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