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A Probabilistic Damage Identification Method for Shear Structure Components Based on Cross-Entropy Optimizations

机译:基于交叉熵优化的剪切结构构件概率损伤识别方法

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A probabilistic damage identification method for shear structure components is presented. The method uses the extracted modal frequencies from the measured dynamical responses in conjunction with a representative finite element model. The damage of each component is modeled using a stiffness multiplier in the finite element model. By coupling the extracted features and the probabilistic structural model, the damage identification problem is recast to an equivalent optimization problem, which is iteratively solved using the cross-entropy optimization technique. An application example is used to demonstrate the proposed method and validate its effectiveness. Influencing factors such as the location of damaged components, measurement location, measurement noise level, and damage severity are studied. The detection reliability under different measurement noise levels is also discussed in detail.
机译:提出了一种剪切结构构件概率损伤识别方法。该方法结合了代表性的有限元模型,使用了从测得的动态响应中提取的模态频率。使用有限元模型中的刚度乘数对每个组件的损坏进行建模。通过将提取的特征与概率结构模型耦合,将损伤识别问题重铸为等效优化问题,可以使用交叉熵优化技术迭代解决该问题。通过一个应用实例来说明所提出的方法并验证其有效性。研究了诸如损坏部件的位置,测量位置,测量噪声水平和损坏严重性等影响因素。还详细讨论了在不同测量噪声水平下的检测可靠性。

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