钢筋混凝土框架结构连续倒塌的竖向非线性动力分析
DOI:
CSTR:
作者:
作者单位:

作者简介:

通讯作者:

中图分类号:

基金项目:

罕遇地震作用下钢筋混凝土框架结构的鲁棒性分析与抗倒塌设计方法(50978080)


Progressive Collapse Analysis of RC Frame Using Vertical Nonlinear Dynamic Analysis
Author:
Affiliation:

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    结构的连续倒塌分析(PCA)是结构抗连续倒塌能力定量评定和抗连续倒塌设计的基础,已成为当前国内外土木工程界的热点研究领域。本文基于备用荷载路径原理,采用考虑构件失效时长的竖向非线性动力分析方法,对钢筋混凝土平面框架结构进行了竖向连续倒塌分析,根据损伤结构的反应判断剩余结构能否抵抗连续倒塌,分析中考虑了将同一轴线不同楼层的框架柱逐根移除和将同一轴线所有楼层的框架柱同时移除这两种情况对结构倒塌失效模式的影响,探讨了失效时长对结构动力响应的影响,发现构件失效时长对剩余结构的响应有重大影响。研究表明,竖向非线性动力分析方法是结构抗连续倒塌设计与抗连续倒塌能力分析的一种有效方法。

    Abstract:

    Progressive Collapse Analysis (PCA) is the base of quantitative assessment of progressive collapse-resisting capacity and progressive collapse-resisting design. There has been heightened interest among researchers of civil engineering in PCA. PCA of planar RC frame is carried on by vertical nonlinear dynamic analysis based on alternate load path method. According to responses of damage structure, progressive collapse-resisting capacity of remaining structure is evaluated. Two cases of removal of elements are analyzed, removal of columns at different stories on the same axes one by one and removal of all columns on the same axes at the same time. According to analysis of the effect of failure duration on structural progressive collapse, it is showed that the response of remaining structure is highly sensitive to failure duration of elements. The results indicate that vertical nonlinear dynamic analysis is an efficient method of progressive collapse-resisting design and progressive collapse-resisting capacity analysis.

    参考文献
    相似文献
    引证文献
引用本文

吕大刚,李雁军,陈志恒.钢筋混凝土框架结构连续倒塌的竖向非线性动力分析[J].土木与环境工程学报(中英文),2012,34(Z1):49-53. LU Da-gang, LI Yan-jun, CHEN Zhi-heng. Progressive Collapse Analysis of RC Frame Using Vertical Nonlinear Dynamic Analysis[J]. JOURNAL OF CIVIL AND ENVIRONMENTAL ENGINEERING,2012,34(Z1):49-53.[doi]

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:
  • 最后修改日期:
  • 录用日期:
  • 在线发布日期: 2012-09-13
  • 出版日期:
文章二维码