您当前的位置:
首页 >
文章列表页 >
Machine learning method for overall stability of welded constant section box columns made of high strength steel
Digital Twins and Intelligent Construction | 更新时间:2023-11-28
    • Machine learning method for overall stability of welded constant section box columns made of high strength steel

    • In the field of research on the overall stability of high-strength steel components, experts have proposed using fiber models to construct databases and machine learning to establish predictive models, significantly improving the accuracy and convenience of predictions.
    • Journal of Civil and Environmental Engineering   Vol. 46, Issue 1, Pages: 182-193(2024)
    • DOI:10.11835/j.issn.2096-6717.2022.131    

      CLC: TU391
    • Received:30 April 2022

      Published:25 February 2024

    移动端阅览

  • ZHANG Yingying,XU Hao,CHEN Peijian,et al.Machine learning method for overall stability of welded constant section box columns made of high strength steel[J].Journal of Civil and Environmental Engineering,2024,46(01):182-193. DOI: 10.11835/j.issn.2096-6717.2022.131.

  •  
  •  

0

Views

10

下载量

0

CSCD

Alert me when the article has been cited
提交
Tools
Download
Export Citation
Share
Add to favorites
Add to my album

Related Articles

Prediction model for horizontal axis deviation of segment during construction stage of small curvature radius shield tunnel
Prediction model of ground settlement caused by construction of double track parallel shield tunnels under arbitrary layout
Prediction of CO2 adsorption performance in porous biochar based on machine learning
Multi-objective optimization design method of modular steel frame structure in cold regions
Review and prospect of machine learning method in shield tunnel construction

Related Author

ZHU Chunzhou
ZOU Jinfeng
WANG Chao
TAN Kanghao
CHEN Yifei
ZHANG Xiaoqing
WANG Junsong
QIN Yinghong

Related Institution

China Railway Shanghai Design Institute Group Co., Ltd.
School of Civil Engineering, Central South University
School of Civil Engineering and Architecture; Key Laboratory of Disaster Prevention and Engineering Safety of Guangxi, Guangxi University
State Key Laboratory of Subtropical Building Science, South China University of Technology
School of Civil and Transportation Engineering; Hebei University of Technology
0