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Neural network-based formula for the buckling load prediction of I-section cellular steel beams

Abambres, M., Rajana, K., Tsavdaridis, K. D. ORCID: 0000-0001-8349-3979 and Ribeiro, T. P. (2019). Neural network-based formula for the buckling load prediction of I-section cellular steel beams. Computers, 8(1), 2. doi: 10.3390/computers8010002

Abstract

Cellular beams are an attractive option for the steel construction industry due to their versatility in terms of strength, size, and weight. Further benefits are the integration of services thereby reducing ceiling-to-floor depth (thus, building’s height), which has a great economic impact. Moreover, the complex localized and global failures characterizing those members have led several scientists to focus their research on the development of more efficient design guidelines. This paper aims to propose an artificial neural network (ANN)-based formula to precisely compute the critical elastic buckling load of simply supported cellular beams under uniformly distributed vertical loads. The 3645-point dataset used in ANN design was obtained from an extensive parametric finite element analysis performed in ABAQUS. The independent variables adopted as ANN inputs are the following: beam’s length, opening diameter, web-post width, cross-section height, web thickness, flange width, flange thickness, and the distance between the last opening edge and the end support. The proposed model shows a strong potential as an effective design tool. The maximum and average relative errors among the 3645 data points were found to be 3.7% and 0.4%, respectively, whereas the average computing time per data point is smaller than a millisecond for any current personal computer.

Publication Type: Article
Additional Information: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
Publisher Keywords: elastic buckling; cellular steel beams; perforated beams; artificial neural networks; finite element analysis
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Departments: School of Mathematics, Computer Science & Engineering > Engineering > Civil Engineering
Date available in CRO: 10 Nov 2021 11:14
Date deposited: 10 November 2021
Date of acceptance: 21 December 2018
Date of first online publication: 26 December 2018
URI: https://openaccess.city.ac.uk/id/eprint/27019
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