|Table of Contents|

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

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

Issue:
2025年05期
Page:
45-54
Research Field:
建筑结构
Publishing date:

Info

Title:
Study on shear capacity of cold-formed thin-walled steel self-drilling screw connections based on machine learning algorithms
Author(s):
WU Hanheng;  WANG Chen;  WANG Tao;  SUI Lu;  ZHOU Tianhua
(School of Civil Engineering, Chang'an University, Xi'an 710061, Shaanxi, China)
Keywords:
cold-formed thin-walled steel;  machine learning;  self-drilling screw connection;  shear capacity
PACS:
TU392.5
DOI:
10.19815/j.jace.2024.04089
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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Last Update: 2025-09-25