基于机器学习的冷弯薄壁卷边槽钢弹塑性屈曲截面尺寸优化

全松; 黄丽华

doi:10.13206/j.gjgS22032502

基于机器学习的冷弯薄壁卷边槽钢弹塑性屈曲截面尺寸优化

doi: 10.13206/j.gjgS22032502

全松,
黄丽华^,

大连理工大学建设工程学部, 辽宁大连 116024

基金项目:

国家自然科学基金资助项目(51678115)

详细信息

作者简介:
全松,男,1996年出生,硕士研究生

通讯作者:
黄丽华,女,1967年出生,硕士,教授,lhhang@dlut.edu.cn。

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- 被引次数: 0
出版历程
- 收稿日期: 2022-03-25
- 网络出版日期: 2023-04-20

Section Optimization of Elastic-Plastic Buckling of Cold-Formed Thin-Walled Lipped Channel Steel Column Based on Machine Learning

QUAN Song,
HUANG Lihua^,

Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian 116024, China

摘要

摘要: 由于影响冷弯薄壁卷边槽钢构件弹塑性屈曲荷载的因素较多,故截面形式与承载力之间无法用精确的解析式表达。为开展截面形式优化,提高槽钢的屈曲承载力,将基因表达式编程(GEP)算法和粒子群(PSO)算法结合开展截面形式优化研究。利用Python编程对ABAQUS进行二次开发,对文献中的15个试验构件进行批量有限元计算,通过与试验值对比验证了数值计算精度;选取其中1组构件为样本开展抗屈曲截面优化,采用批量有限元计算生成包含不同截面尺寸和对应屈曲荷载的样本数据共97组;采用基因表达式编程算法对数据集样本进行数据拟合,构建截面优化目标函数的代理模型;利用粒子群优化算法得到冷弯薄壁卷边槽钢弹塑性屈曲最大承载力对应的最优截面尺寸。与优化前相比,截面优化后构件的屈曲承载力提升了30.4%。研究表明,将有限元、机器学习和传统优化方法相结合,能够有效开展薄壁槽钢的截面形式优化。
- 冷弯薄壁槽钢 /
- 屈曲荷载 /
- 截面优化 /
- GEP算法 /
- PSO算法
Abstract: There are many factors that affect the elastic-plastic buckling capacity of cold-formed thin-wall steel lipped channel members. However, the relationship between section form and bearing capacity cannot be accurately expressed analytically. In order to optimize the section form and improve the buckling capacity of channel steel, the gene expression programming(GEP) algorithm and the particle swarm optimization(PSO) algorithm were combined to optimize section form. Through calling ABAQUS by Python programming, the finite element calculation of 15 test components in the literature was carried out in batches. The accuracy of the numerical solution was verified by comparing with the experimental values. A group of components were selected as samples to carry out anti-buckling section optimization. A total of 97 ensembles of sample data including different section sizes and corresponding buckling loads were generated by the batch finite element calculation. The gene expression programming algorithm was used to fit the dataset, and the surrogate model of the objective function of cross-section optimization was constructed. The particle swarm optimization algorithm was used to obtain the optimal section size corresponding to the maximum elastoplastic buckling capacity of cold-formed thin-walled lipped channel steel. Compared with the original specimen, the buckling capacity is increased by 30.4% after section optimization. The results show that the combination of finite element, machine learning and traditional optimization methods can be adopted to effectively optimize the section form of thin-walled channel column.
- cold-formed thin-walled channel steel column /
- buckling load /
- section optimization /
- GEP algorithm /
- PSO algorithm

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参考文献(31)

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