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An assessment of data quality for structurla identification

机译:评估数据质量以进行结构识别

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Structural identification is a popular method for nondestructive condition assessemtn of existing structural facilities. Two significant error sources in the practical application of structural identification are uncertainties in measured data and modeling error. Uncertainties in the experimental data and the overall quality of the data are addressed in this paper. It is widely accepted that noisy and inaccurate measurements can lead to inaccuracies in the identified parameters and possibly divergence. A set of quality indices is proposed to provide a consistent and quantifiable measure of data quality. The indices are defined to represent the physical characteristics of the structural system and to highlight data qualities that are significant in structural identification. The consistent measure makes it possible to compare different data channels and test scenarios applicable to structural parameter estimation. The data quality indices presented i nthis paper are applied to experimenal test data obtained from a laboratory grid structure. Quality indices from different sets of test data are used as a rational basis for the comparison.
机译:结构识别是一种对现有结构设施进行非破坏性状态评估的流行方法。在结构识别的实际应用中,两个重要的误差来源是测量数据的不确定性和建模误差。本文解决了实验数据的不确定性和数据的整体质量。噪声和不准确的测量结果可能会导致所确定的参数不正确,甚至可能导致差异,这一点已被广泛接受。提出了一组质量指标,以提供一致且可量化的数据质量度量。定义索引以表示结构系统的物理特征并突出显示对结构标识很重要的数据质量。一致的度量使得可以比较适用于结构参数估计的不同数据通道和测试方案。本文提出的数据质量指标适用于从实验室网格结构获得的实验测试数据。来自不同测试数据集的质量指标被用作进行比较的合理基础。

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