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Web-Post Buckling Prediction Resistance of Steel Beams with Elliptically-Based Web Openings using Artificial Neural Networks (ANN)

Shamass, R., Ferreira, F. P. V., Limbachiya, V. , Santos, L. F. P. & Tsavdaridis, K. D. ORCID: 0000-0001-8349-3979 (2022). Web-Post Buckling Prediction Resistance of Steel Beams with Elliptically-Based Web Openings using Artificial Neural Networks (ANN). Thin Walled Structures,

Abstract

This paper aims to propose an Artificial Neural Network (ANN) model that predicts accurately web-post buckling resistance and failure mode of steel beams with elliptically-based openings. A total of 4,344 and 5,400 geometrical models, were developed by finite element method (FEM) and used to train, validate and test the ANN model for the web-post resistance and failure mode classification, respectively. It was concluded that five neurons model were sufficient to predict the web-post buckling resistance and the failure mode with high level of accuracy. The height and the web thickness of the beams had positive impact of the capacity while the web openings height, width and radius of the elliptically-based web opening were the geometric parameters that had negative impact of the capacity. At last, an ANN-based formula was proposed and compared with previous analytical model for web-post buckling resistance of elliptically-based openings, which considered the web-post as a truss model. The ANN-based formula showed high accuracy, since the Regression (R²), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Average (FEM/Predicted), Standard Deviation and Variation were 0.9989, 26.03 kN, 15.0 kN, 1.00, 4% and 0.12%, respectively. Consequently, the ANN-based formula for web-post buckling resistance of steel beams with elliptically-based openings can be safely adopted for design purposes.

Publication Type: Article
Additional Information: © 2022. This article has been accepted for publication in Thin-Walled Structures by Elsevier. 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: Artificial neural network; Classifications; Machine learning; Perforated beams, Elliptically-based web openings; Web-post buckling.
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TH Building construction
Departments: School of Science & Technology > Engineering
[img] Text - Accepted Version
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