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A multi-objective framework for finite element model updating using incomplete modal measurements

机译：使用不完整模态测量更新有限元模型的多目标框架

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摘要

Finite element (FE) model updating in multi-objective framework helps for better understanding of overall performance in updating (under various variations of weightages assigned to basic components of the objective function) along with providing scope for better judgmental selection. A FE model updating in multi-objective framework is proposed with no requirement of repeated eigen-solution along with avoiding repeated possibilities of incurring mode-pairing error (by adopting an existing framework of system mode shape). Two multi-objective optimization techniques are adopted: (a) weighted sum and (b) adaptive weighted sum methods. Moreover, a possible single best solution out of the Pareto front is identified based on minimum modal distance value and compared with Gibbs sampling technique (without mode-matching). Two examples with multiple damage cases utilized in validating the proposed approach are as follows: (a) simulated example (ASCE benchmark structure) and (b) experimental example (four storied shear frame laboratory structure). It is observed that the proposed multi-objective framework has performed well in FE model updating in case of both simulated and experimental cases. Additionally, a connection (directly relating the multi-objective weights and error variances) is established between the proposed updating methodology and an existing Bayesian updating methodology to facilitate the probabilistic damage detection in Bayesian framework. Moreover, selection of an appropriate solution (out of the Pareto front) having suitable values of multi-objective weights facilitates to estimate the suitable values of error variances (based on the proposed connection), consequently enabling an efficient Bayesian FE model updating without requirement of any assumption of error variances.
机译：多目标框架的有限元（FE）模型更新有助于更好地了解更新时的整体性能（在分配给客观函数的基本组件的重量各种变化下）以及提供更好的判断选择的范围。提出了多目标框架中的FE模型更新，不需要重复的特征解决方案以及避免了导致模式配对误差的重复可能性（通过采用现有的系统模式形状框架）。采用两种多目标优化技术：（a）加权和（b）自适应加权和方法。此外，基于最小模态距离值并与Gibbs采样技术（无模式匹配）相比，识别出帕累托前部的可能的单个最佳溶液。具有验证所提出的方法的多种损伤案例的两个示例如下：（a）模拟示例（Asce基准结构）和（b）实验例（四个沉积的剪切架实验室结构）。观察到，在模拟和实验情况下，所提出的多目标框架在Fe模型更新中表现良好。另外，在所提出的更新方法和现有的贝叶斯更新方法之间建立连接（直接相关的多目标权重和错误差异），以促进贝叶斯框架中的概率损伤检测。此外，选择具有合适的多目标重量值的适当的解决方案（从帕累托前沿）有助于估计误差方差的合适值（基于所提出的连接），因此可以在不需要的情况下实现有效的贝叶斯FE模型更新任何错误差异的假设。

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