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Automated Finite Element Model Updating of the UCF Grid Benchmark Using Multiresponse Parameter Estimation.

机译:使用多响应参数估计对UCF网格基准进行自动有限元模型更新。

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摘要

Structural Health Monitoring (SHM) using nondestructive test (NDT) data has become very promising for finite element (FE) model updating, model verification, structural evaluation, and damage assessment. This research presents a multiresponse structural parameter estimation method for the FE model updating using data obtained from a nondestructive test on a laboratory bridge model. Having measurement and modeling errors is an inevitable part of data acquisition systems and finite element models. The presence of these errors can affect the accuracy of the parameter estimates. Therefore, an error sensitivity analysis using Monte Carlo simulation was used to study the input-output error behavior of each parameter based on the load cases and measurement locations of the nondestructive tests. Given the measured experimental responses, the goal was to select the unknown parameters of the FE model with high observability that leads to creating a well-conditioned system with the least sensitivity to measurement errors. A data quality study was performed to assess the accuracy and reliability of the measured data. Based on this study, a subset of the most reliable measured data was selected for the FE model updating. The selected subset of higher quality measurements and the observable unknown parameters were used for FE model updating. Three static and dynamic error functions were used for structural parameter estimation using the selected measured static strains, displacements, and slopes as well as dynamic natural frequencies and associated mode shapes. The measured data sets were used separately and also together for multiresponse FE model updating to match the predicted analytical response with the measured data. The FE model was successfully calibrated using multiresponse data. Two separate commercially available software packages were used with real-time data communications utilizing Application Program Interface (API) scripts. This approach was efficient in utilizing these software packages for automated and systematic FE model updating. This method is applicable to full-scale structures and can be used for bridge model validation and bridge management.
机译:使用非破坏性测试(NDT)数据的结构健康监测(SHM)对于有限元(FE)模型更新,模型验证,结构评估和损伤评估而言非常有希望。这项研究提出了一种用于有限元模型更新的多响应结构参数估计方法,该方法使用从实验室桥梁模型的无损测试中获得的数据进行更新。存在测量和建模错误是数据采集系统和有限元模型的必然部分。这些错误的存在会影响参数估计的准确性。因此,基于蒙特卡洛模拟的误差敏感性分析被用于基于无损测试的载荷情况和测量位置来研究每个参数的输入输出误差行为。给定测量的实验响应,目标是选择具有高可观察性的有限元模型的未知参数,从而创建对测量误差敏感度最低的条件良好的系统。进行了数据质量研究,以评估测量数据的准确性和可靠性。根据这项研究,选择了最可靠的测量数据的一个子集进行FE模型更新。选择的较高质量的测量子集和可观察的未知参数用于有限元模型更新。使用选定的测得的静态应变,位移和斜率以及动态固有频率和相关的振型,将三个静态和动态误差函数用于结构参数估计。测量数据集分别使用,也一起用于多响应FE模型更新,以使预测的分析响应与测量数据匹配。 FE模型已使用多响应数据成功校准。利用应用程序接口(API)脚本,两个单独的可商购软件包与实时数据通信一起使用。这种方法有效地利用了这些软件包来进行自动化和系统的有限元模型更新。此方法适用于大型结构,可用于桥梁模型验证和桥梁管理。

著录项

  • 作者

    Khaloo, Ali.;

  • 作者单位

    Tufts University.;

  • 授予单位 Tufts University.;
  • 学科 Engineering Civil.
  • 学位 M.S.
  • 年度 2014
  • 页码 138 p.
  • 总页数 138
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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