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Wavelet‐based approach of time series model for modal identification of a bridge with incomplete input
Computer-Aided Civil and Infrastructure Engineering ( IF 9.6 ) Pub Date : 2020-02-17 , DOI: 10.1111/mice.12539
C. S. Huang, Q. T. Le, W. C. Su, C. H. Chen

Identification of modal parameters of a bridge from its earthquake responses is crucial for performing damage assessment of the structure. However, all the input base excitations of the bridge may not be measured because of economic concerns and sensor malfunctions. Consequently, evaluating the modal parameters of a bridge under the consideration of incomplete input measurements is a challenging and important task. An approach that combines the continuous Cauchy wavelet transform with an autoregressive time‐varying moving average with exogenous input (AR‐TVMA‐X) model is proposed in this study to identify the modal parameters of a multispan bridge under multiple support earthquake excitations with incomplete measurements. The efficiency and efficacy of the proposed approach are first validated using numerically simulated responses of a three‐span continuous beam subjected to multiple support nonstationary excitations. A standard procedure of using the proposed approach to identify the modal parameters is established according to comprehensive studies on the effects of noise in the data, the number of supports whose excitations are used in the AR‐TVMA‐X model, and the orders of the AR‐TVMA‐X model on the accuracy of identifying the modal parameters. This procedure is further applied to process the earthquake responses of a two‐span cable‐stayed 510‐m‐long bridge to demonstrate the engineering applicability of the proposed approach.

中文翻译:

基于时间序列模型的小波方法用于不完全输入的桥梁的模态识别

从桥梁的地震响应中识别模态参数对于进行结构的损伤评估至关重要。但是,由于经济原因和传感器故障,可能无法测量电桥的所有输入基极激励。因此,在考虑不完整输入测量的情况下评估桥梁的模态参数是一项艰巨而重要的任务。这项研究提出了一种将连续柯西小波变换与具有外生输入的自回归时变移动平均值(AR-TVMA-X)模型相结合的方法,以在不完整测量的情况下识别多支撑地震激励下的多跨桥梁的模态参数。首先使用受到多个支撑非平稳激励的三跨连续梁的数值模拟响应来验证所提出方法的效率和功效。根据对数据中噪声的影响,AR‐TVMA‐X模型中使用了激励的支座的数量以及阶数的综合研究,建立了使用提议的方法识别模态参数的标准程序。 AR‐TVMA‐X模型可用于识别模态参数的准确性。该程序还用于处理两跨510米长斜拉桥的地震响应,以证明所提出方法的工程适用性。根据对数据中噪声的影响,AR‐TVMA‐X模型中使用了激励的支座的数量以及阶数的综合研究,建立了使用提议的方法识别模态参数的标准程序。 AR‐TVMA‐X模型可用于识别模态参数的准确性。该程序还用于处理两跨510米长斜拉桥的地震响应,以证明所提出方法的工程适用性。根据对数据中噪声的影响,AR‐TVMA‐X模型中使用了激励的支座的数量以及阶数的综合研究,建立了使用提议的方法识别模态参数的标准程序。 AR‐TVMA‐X模型可用于识别模态参数的准确性。该程序还用于处理两跨510米长斜拉桥的地震响应,以证明所提出方法的工程适用性。
更新日期:2020-02-17
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