屈曲
结构工程
桁架
欧洲规范
梁(结构)
参数统计
有限元法
人工神经网络
压缩(物理)
计算机科学
工程类
材料科学
复合材料
人工智能
数学
统计
作者
Musab Rabi,Ikram Abarkan,Rabee Shamass
标识
DOI:10.1002/stco.202200036
摘要
Abstract The use of circular hollow sections (CHS) has increased in recent years owing to its excellent mechanical behaviour including axial compression and torsional resistance as well as its aesthetic appearance. They are popular in a wide range of structural members, including beams, columns, trusses and arches. The behaviour of hot‐finished CHS beam‐columns made from normal‐ and high‐strength steels is the main focus of this article. A particular attention is given to predict the ultimate buckling resistance of CHS beam‐columns using the recent advancement of the artificial neural network (ANN). Finite element (FE) models were established and validated to generate an extensive parametric study. The ANN model is trained and validated using a total of 3439 data points collected from the generated FE models and experimental tests available in the literature. A comprehensive comparative analysis with the design rules in Eurocode 3 is conducted to evaluate the performance of the developed ANN model. It is shown that the proposed ANN‐based design formula provides a reliable means for predicting the buckling resistance of the CHS beam‐columns. This formula can be easily implemented in any programming software, providing an excellent basis for engineers and designers to predict the buckling resistance of the CHS beam–columns with a straightforward procedure in an efficient and sustainable manner with least computational time.
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