...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International journal of structural stability and dynamics >Damage Identification of Periodically-Supported Structures Following the Bayesian Probabilistic Approach
【24h】

Damage Identification of Periodically-Supported Structures Following the Bayesian Probabilistic Approach

机译：贝叶斯概率方法后定期支持结构的损害识别

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

This paper presents a probabilistic damage identification methodology tailor-made for periodically-supported structures with finite-length. The free wave motion of a general periodically-supported structure with a single disorder is analyzed through the characteristic receptance approach, and the corresponding frequency characteristic equation is developed. In addition, a concept of nondimensional frequency is introduced, and the sensitivity matrix of the nondimensional frequencies with respect to changes in stiffness of periodic cells is obtained by solving the frequency characteristic equation and utilizing the sensitivity analysis technique. Following the sensitivity-based identification equation with nondimensional frequency information, the probabilistic methodology for identifying the damage occurring in the periodically-supported structures is developed by implementing the Bayesian approach and the Markov chain Monte Carlo (MCMC) simulation with the Metropolis-Hasting sampling algorithm. The validity of the proposed methodology is demonstrated by both numerical simulations for a periodically-supported flanged pipeline example and experimental case studies conducted for a multi-span aluminum beam model endowed with bolted connections in the laboratory.
机译：本文介绍了概率损伤识别方法，用于定期支撑的结构，具有有限长度。通过特征接收方法分析具有单一病症的一般定期支持的结构的自由波运动，并且开发了相应的频率特性方程。另外，通过求解频率特性方程并利用灵敏度分析技术，引入了一种非幂频率的概念，并且通过求解频率特性方程来获得与周期性细胞刚度变化的敏感性矩阵。在具有非潜能频率信息的基于灵敏度的识别方程之后，通过实现贝叶斯方法和Markov链蒙特卡罗（MCMC）仿真与Metropolic-Hasting采样算法进行识别在周期性地支持的结构中发生损坏的概率方法。通过用于定期支撑的法兰管线示例的数值模拟来证明所提出的方法的有效性以及用于在实验室中赋予螺栓连接的多跨度铝束模型进行的实验性壳体研究。

著录项

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