Modal identification with uncertainty quantification of large‐scale civil structures via a hybrid operation modal analysis framework

Mengmeng Sun, Qiusheng Li
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Abstract

SummaryIn operational modal analysis (OMA), only structural responses are typically available. In this context, bias and variance (uncertainty) errors may exist in modal estimates (especially damping estimates), resulting in inaccurate determination of the modal properties of large‐scale structures under harsh excitations. To this end, a hybrid OMA framework based on the modal decoupling, the natural excitation technique, the random decrement technique (RDT), and improved eigensystem realization algorithm (ERA) with the automated stabilization diagram is presented to perform high‐accuracy modal estimates with uncertainty quantification for large‐scale structures under normal and severe ambient excitations. The accuracy and effectiveness of the hybrid framework for identifying the modal parameters are validated by numerical simulation study of a framework structural model. Furthermore, the hybrid framework is applied to analyze recorded acceleration responses of a supertall building with 600‐m height under normal excitations and typhoon condition to verify its applicability in field measurements. The numerical simulation and field measurement studies demonstrate that the hybrid framework can not only perform precise modal estimations with uncertainty quantification through a single ambient vibration measurement but also effectively reveal the variations of modal properties of supertall structures under harsh excitations from multiple perspectives. This paper aims to enhance the reliability and accuracy of modal estimation for engineering structures and further provide insight into the variations of dynamic properties of large‐scale civil structures under severe excitations.
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通过混合运行模态分析框架对大型民用结构进行模态识别和不确定性量化
摘要在运行模态分析(OMA)中,通常只能获得结构响应。在这种情况下,模态估计值(尤其是阻尼估计值)可能存在偏差和方差(不确定性)误差,导致无法准确确定大型结构在恶劣激励下的模态特性。为此,本文提出了一种基于模态解耦技术、自然激励技术、随机递减技术(RDT)和带有自动稳定图的改进型特征系统实现算法(ERA)的混合 OMA 框架,用于在正常和恶劣环境激励下对大型结构进行高精度模态估算和不确定性量化。通过对框架结构模型进行数值模拟研究,验证了混合框架在确定模态参数方面的准确性和有效性。此外,混合框架还被应用于分析一座 600 米高的超高层建筑在正常激励和台风条件下的加速度响应,以验证其在现场测量中的适用性。数值模拟和现场测量研究表明,混合框架不仅能通过单次环境振动测量进行精确的模态估算和不确定性量化,还能从多个角度有效揭示超高层结构在恶劣激励下的模态特性变化。本文旨在提高工程结构模态估算的可靠性和准确性,并进一步揭示大型民用结构在恶劣激励下的动态特性变化。
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