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Modal parameter identification of a multiple-span post-tensioned concrete bridge using hybrid vibration testing data

机译:混合振动测试数据的多跨后柱柱柱柱柱柱的模态参数识别

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The paper describes and evaluates application of output-only system identification to an eleven-span post-tensioned concrete bridge using hybrid excitation. A linear chirp sweeping force, induced by two light-weight electro-dynamic shakers, augmented environmental sources to excite the bridge during the hybrid testing exercise. To obtain the modal characteristics of the structure, two output-only time domain system identification methods were employed, namely auto-regressive (AR) time series model and eigensystem realization algorithm with observer/Kalman identification (ERA-OKID), with the traditional data-driven stochastic subspace identification method (SSI-data) providing a comparative benchmark. The accuracy and efficiency of both system identification algorithms when used on hybrid testing data are investigated and compared to the results from purely ambient vibration testing data. The study demonstrates that using both output-only identification algorithms the collected vibration responses induced by the proposed hybrid vibration testing methodology can be used for extracting modal parameters with enhanced accuracy and reliability (i.e. more identified modes) for the large-scale post-tensioned concrete bridge due to the increase in the excitation strength and better coverage of the relevant frequency bands. Compared to the classical SSI-data, the AR and ERA-OKID techniques were able to identify more modes at reduced computational cost when applied to voluminous data from multi-channel measurements.
机译:本文介绍了使用混合励磁的11个跨度后张紧混凝土桥的应用。由两个轻型电动振动器引起的线性啁啾扫描力,在混合检测运动期间增强环境来源激发桥梁。为了获得结构的模态特性,使用了两个输出时域系统识别方法,即自动回归(AR)时间序列模型和具有传统数据的观察者/卡尔曼识别(ERA-Okid)的Eigensysy系统实现算法 - 提供比较基准的随机子空间识别方法(SSI数据)。研究了在混合测试数据上使用时系统识别算法的准确性和效率,并与纯环境振动测试数据的结果进行了比较。该研究表明,仅使用仅输出的识别算法,所提出的混合振动试验方法引起的收集的振动响应可用于提取模态参数,以提高大规模后张紧混凝土的提高精度和可靠性(即更多识别的模式)桥梁由于激励强度的增加和相关频带的更好覆盖范围。与经典SSI数据相比,AR和ERA-OKID技术能够在从多通道测量中应用于大量数据时以降低的计算成本识别更多模式。

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