Uncertainty quantification and propagation in dynamic models using ambient vibration measurements, application to a 10-story building
Uncertainty quantification and propagation in dynamic models using ambient vibration measurements, application to a 10-story building
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DOI：
10.1016/j.ymssp.2018.01.033
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发表时间：
2018-07
影响因子：
8.4
通讯作者：
I. Behmanesh;Seyedsina Yousefianmoghadam;A. Nozari;B. Moaveni;A. Stavridis
中科院分区：
文献类型：
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作者：
I. Behmanesh;Seyedsina Yousefianmoghadam;A. Nozari;B. Moaveni;A. Stavridis
本文研究了多层贝叶斯模型修正在土木结构不确定性量化和响应预测中的应用。在该更新框架中，初始有限元（FE）模型的结构参数（例如，刚度或质量）通过最小化所识别的模态参数和模型的相应参数之间的误差函数来校准。假设这些误差函数具有高斯概率分布，其中未知参数有待确定。误差函数的估计参数表示校准模型在预测建筑物响应时的不确定性（此处为模态参数）。本文的重点是回答在建筑物的参考/校准状态下使用动态测量的量化模型不确定性是否可以用于提高不同结构状态下的模型预测精度，例如，损坏的结构。此外，预测误差偏差的预测值的不确定性的影响进行了研究。这里考虑的测试结构是位于纽约州尤蒂卡的一栋十层混凝土建筑。从环境振动数据中识别建筑物在参考状态下的模态参数，并用于校准初始有限元模型的参数以及误差函数。在拆除该建筑物之前，拆除了其六面外墙，并在拆除墙壁后从结构中收集了环境振动测量值。这些数据不用于校准模型;它们仅用于评估预测结果。本文提出的模型修正框架被应用于估计的模态参数的建筑物在其参考状态以及两个损坏状态：中度损坏（拆除四面墙）和严重损坏（拆除六面墙）。模型预测的模态参数和振动试验确定的模态参数之间有很好的一致性。此外，它表明，包括预测误差偏差在更新过程中，而不是常用的零均值误差函数可以显着降低预测的不确定性。
This paper investigates the application of Hierarchical Bayesian model updating for uncertainty quantification and response prediction of civil structures. In this updating framework, structural parameters of an initial finite element (FE) model (e.g., stiffness or mass) are calibrated by minimizing error functions between the identified modal parameters and the corresponding parameters of the model. These error functions are assumed to have Gaussian probability distributions with unknown parameters to be determined. The estimated parameters of error functions represent the uncertainty of the calibrated model in predicting building’s response (modal parameters here). The focus of this paper is to answer whether the quantified model uncertainties using dynamic measurement at building’s reference/calibration state can be used to improve the model prediction accuracies at a different structural state, e.g., damaged structure. Also, the effects of prediction error bias on the uncertainty of the predicted values is studied. The test structure considered here is a ten-story concrete building located in Utica, NY. The modal parameters of the building at its reference state are identified from ambient vibration data and used to calibrate parameters of the initial FE model as well as the error functions. Before demolishing the building, six of its exterior walls were removed and ambient vibration measurements were also collected from the structure after the wall removal. These data are not used to calibrate the model; they are only used to assess the predicted results. The model updating framework proposed in this paper is applied to estimate the modal parameters of the building at its reference state as well as two damaged states: moderate damage (removal of four walls) and severe damage (removal of six walls). Good agreement is observed between the model-predicted modal parameters and those identified from vibration tests. Moreover, it is shown that including prediction error bias in the updating process instead of commonly-used zero-mean error function can significantly reduce the prediction uncertainties.