Asteris, PG, Lemonis, ME, Le, T-T et al. (1 more author) (2021) Evaluation of the Ultimate Eccentric Load of Rectangular CFSTs using Advanced Neural Network Modeling. Engineering Structures, 248. 113297. ISSN 0141-0296
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.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2021 Elsevier Ltd. This is an author produced version of an article published in Engineering Structures. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Concrete-Filled Steel Tube (CFST); Artificial neural networks (ANNs); Load eccentricity; Rectangular CFST; Ultimate load |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Civil Engineering (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 27 Sep 2021 14:40 |
Last Modified: | 06 Apr 2023 09:28 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.engstruct.2021.113297 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:178502 |
Download
Filename: 210823 Full Manuscript_to Symplecticdocx.pdf
Licence: CC-BY-NC-ND 4.0