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首页> 外文期刊>Mechanical systems and signal processing >Probability-based damage detection using model updating with efficient uncertainty propagation
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Probability-based damage detection using model updating with efficient uncertainty propagation

机译：使用有效的不确定性传播进行模型更新的基于概率的损坏检测

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摘要

Model updating method has received increasing attention in damage detection of structures based on measured modal parameters. In this article, a probability-based damage detection procedure is presented, in which the random factor method for non-homogeneous random field is developed and used as the forward propagation to analytically evaluate covariance matrices in each iteration step of stochastic model updating. An improved optimization algorithm is introduced to guarantee the convergence and reduce the computational effort in which the design variables are restricted in search region by region truncation of each iteration step. The developed algorithm is illustrated by a simulated 25-bar planar truss structure and the results have been compared and verified with those obtained from Monte Carlo simulation. In order to assess the influences of uncertainty sources on the results of model updating and damage detection of structures, a comparative study is also given under different cases of uncertainties, that is, structural uncertainty only, measurement uncertainty only and combination of the two. The simulation results show the proposed method can perform well in stochastic model updating and probability-based damage detection of structures with less computational effort.
机译：基于测量的模态参数的模型更新方法在结构损伤检测中受到越来越多的关注。在本文中，提出了一种基于概率的损伤检测程序，其中开发了非均匀随机场的随机因子方法，并将其用作前向传播，以分析评估随机模型更新的每个迭代步骤中的协方差矩阵。引入了一种改进的优化算法，以保证收敛性并减少计算量，其中通过每个迭代步骤的区域截断将设计变量限制在搜索区域中。通过模拟的25杆平面桁架结构说明了所开发的算法，并将结果与​​从Monte Carlo仿真获得的结果进行了比较和验证。为了评估不确定性来源对模型更新和结构损伤检测结果的影响，还对不同情况下的不确定性进行了比较研究，即仅结构不确定性，仅测量不确定性以及两者的组合。仿真结果表明，该方法在随机模型更新和基于概率的结构损伤检测中具有良好的性能，且计算量较小。

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