...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International journal of steel structures >A Comparative Study on the Performance of FEM, RA and ANN Methods in Strength Prediction of Pallet-Rack Stub Columns
【24h】

A Comparative Study on the Performance of FEM, RA and ANN Methods in Strength Prediction of Pallet-Rack Stub Columns

机译：有限元，RA和ANN方法性能对托盘齿轮柱柱强度预测性能的比较研究

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The rack column is one of the essential elements in the pallet rack system. However, due to its distinctive perforation feature, it is challenging to analyze its stability using traditional theories for cold-formed steel structures. In this paper, we are interested in the comparison analysis of strength prediction on the perforated columns using finite element method (FEM), regression analysis (RA) and artificial neural network (ANN) methods respectively. First, a refined finite element (FE) model considering the perforation and nonlinearity behavior was generated and calibrated against the experimental results. Subsequently, the validated FE model was used to perform the parametric analysis for the different holes in columns. Given experimental and simulated data, a regression model with an equivalent thickness was proposed for the design strength prediction of thin-walled steel perforated sections. For comparison of the RA model, two powerful tools such as the FEM and ANN are also employed to predict the design strength of different perforated sections. Four indicators were used to assess the accuracy and generalization performance of the three models, including the root mean square error, the mean absolute percentage error, the correlation coefficient and the mean relative percentage. The obtained results show that although they both have good consistency, FEM still slightly outperforms the other two models. Since the values calculated from ANN and regression models are usually smaller than the experimental data, they are reasonably recommended as effective and safer design tools than FEM models from the perspective of engineering applications.
机译：机架列是托盘架系统中的基本元素之一。然而，由于其独特的穿孔特征，使用传统的冷成型钢结构的理论来分析其稳定性挑战。在本文中，我们对使用有限元方法（FEM），回归分析（RA）和人工神经网络（ANN）方法的穿孔柱对电力预测的比较分析感兴趣。首先，考虑到穿孔和非线性行为的精制有限元（Fe）模型被产生并校准实验结果。随后，使用验证的FE模型用于对列中的不同孔进行参数分析。考虑到实验和模拟数据，提出了一种具有等同厚度的回归模型，用于薄壁钢穿孔部分的设计强度预测。为了比较RA模型，还采用了两个强大的工具，例如FEM和ANN，以预测不同穿孔部分的设计强度。四个指标用于评估三种模型的准确性和泛化性能，包括根均方误差，平均绝对百分比误差，相关系数和平均相对百分比。所获得的结果表明，虽然它们都具有良好的一致性，但有限元素仍然略微优于另外两种型号。由于从ANN和回归模型计算的值通常小于实验数据，因此从工程应用的角度来看，它们与FEM模型合理地建议使用比FEM模型更有效和更安全的设计工具。

著录项

  • 来源
    《International journal of steel structures》 |2020年第5期|1509-1526|共18页
  • 作者

    ZhiJun Lyu; Jie Zhang; Ning Zhao; Qian Xiang; YiMing Song; Jie Li;

  • 作者单位

    College of Mechanical Engineering Donghua University Shanghai 201620 China Shanghai Engineering Research Centre of Storage and Logistics Equipment Shanghai 201611 China;

    College of Mechanical Engineering Donghua University Shanghai 201620 China Shanghai Engineering Research Centre of Storage and Logistics Equipment Shanghai 201611 China;

    College of Mechanical Engineering Donghua University Shanghai 201620 China Shanghai Engineering Research Centre of Storage and Logistics Equipment Shanghai 201611 China;

    College of Mechanical Engineering Donghua University Shanghai 201620 China Shanghai Engineering Research Centre of Storage and Logistics Equipment Shanghai 201611 China;

    Shanghai Motor Vehicle Testing Center Technology Co. Ltd Shanghai 201805 China;

    College of Mechanical Engineering Donghua University Shanghai 201620 China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Strength prediction; Regression analysis (RA); Finite element method (FEM); Artificial neural network (ANN); Thin-walled steel perforated sections;

    机译：强度预测;回归分析（RA）;有限元方法（FEM）;人工神经网络（ANN）;薄壁钢穿孔部分;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号