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《建筑科学与工程学报》[ISSN:1673-2049/CN:61-1442/TU]

卷:

42卷

期数:

2025年05期

页码:

45-54

栏目:

建筑结构

出版日期:

2025-09-30

文章信息/Info

Title:

Study on shear capacity of cold-formed thin-walled steel self-drilling screw connections based on machine learning algorithms

文章编号:

1673-2049(2025)05-0045-10

作者:

吴函恒,王辰,王涛,隋璐,周天华

(长安大学 建筑工程学院,陕西 西安 710061)

Author(s):

WU Hanheng, WANG Chen, WANG Tao, SUI Lu, ZHOU Tianhua

(School of Civil Engineering, Chang'an University, Xi'an 710061, Shaanxi, China)

关键词:

冷弯薄壁型钢;  机器学习;  自攻螺钉连接;  受剪承载力

Keywords:

cold-formed thin-walled steel;  machine learning;  self-drilling screw connection;  shear capacity

分类号:

TU392.5

DOI:

10.19815/j.jace.2024.04089

文献标志码:

A

摘要:

为了提高冷弯薄壁型钢自攻螺钉连接受剪承载力预测的准确性,提出了一种基于机器学习算法的受剪承载力预测模型。在试验的基础上,以钉头及钉尾处钢板厚度、螺钉直径和钢板屈服强度作为影响因素,基于BP神经网络和支持向量回归(SVR)算法对试验数据进行训练,得到受剪承载力预测模型,并将模型预测结果与试验值及规范计算值进行对比。结果表明:两种基于机器学习算法的模型均能较为准确地预测自攻螺钉连接的受剪承载力,具有较高的预测精度,并且模型泛化能力强,而规范公式计算值偏为保守; 与基于BP神经网络算法的承载力预测模型相比,基于SVR算法的模型预测精度优于BP神经网络,其拟合优度提升了3.5%,均方根误差减小了27%,平均绝对误差减小了13%; 研究成果可为实际工程应用提供参考。

Abstract:

In order to improve the accuracy of shear capacity prediction of cold-formed thin-walled steel self-drilling screw connection, a shear capacity prediction model based on machine learning algorithm was proposed. Based on tests, taking thickness of steel plates contacting with screw heads and screw tails as well as diameter of screws as factors, the BP neural network and support vector regression(SVR)algorithms were used to train the test data. Then the prediction models of shear bearing capacity were obtained. The prediction results were compared with test values and code values. The results show that the two machine learning algorithms can predict the shear capacity more accurately and have high prediction accuracy. The models have a strong generalization ability, but the calculated values based on related codes are conservative. Compared with the prediction model of bearing capacity based on BP neural network algorithm, the fit goodness based on SVR algorithm is improved by 3.5%, the root mean square error is reduced by 27%, and the mean absolute error is reduced by 13%, which indicate that the prediction accuracy of the SVR model is better than that of BP neural network. The research findings can provide references for application of actual engineering.

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备注/Memo

备注/Memo:

收稿日期:2024-04-26 投稿网址:http://jace.chd.edu.cn
基金项目:国家自然科学基金项目(51508029,51878055); 陕西省自然科学基础研究计划项目(2023-JC-YB-295);
中央高校基本科研业务费专项资金项目(300102282204,300102282718)
作者简介:吴函恒(1984-),男,工学博士,副教授,E-mail:wuhanheng@163.com。
Author resume: WU Hanheng(1984-), male, PhD, associate professor, E-mail: wuhanheng@163.com.

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更新日期/Last Update: 2025-09-25