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Operational modal analysis of an eleven-span concrete bridge subjected to weak ambient excitations

机译:弱环境激励下十一跨混凝土桥梁的运行模态分析

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The challenges of accurately identifying the dynamic characteristics of bridge structures from ambient vibration responses persist because, unlike in the ideal laboratory environment, elevated levels of noise in data are unavoidable at any in-situ testing site and may have detrimental effects on the modal parameter identification process and results. This is especially true for vibration tests conducted under weak ambient excitation sources resulting in poorer signal-to-noise ratios (SNRs). This paper presents an investigation into the feasibility and reliability of modal identification using operational modal analysis (OMA) techniques under such weak excitation circumstances and with responses measured by inexpensive stand-alone accelerometers/data recorders. An eleven-span concrete motorway off-ramp bridge, closed to traffic, was excited only by ground vibrations generated by traffic on the motorway passing underneath the bridge as well as on nearby motorway on- and off-ramps, weak winds, and possible micro tremors. A high spatial resolution of measuring points on the bridge deck was used to collect vibration responses. Three output-only modal parameter identification algorithms were utilised to extract the modal properties, namely the peak picking (PP), the frequency domain decomposition (FDD) and the data driven stochastic subspace identification (SSI) method. Nine lateral and three vertical modal frequencies below 10 Hz could be identified despite the weakness of the environmental excitations and noise in sensors. The identified experimental natural frequencies were stable, damping ratios, however, had a marked scatter. A comparison with the results of a numerical modal analysis using a finite element model revealed, however, that several higher order vertical modes were missing from the experimental results altogether, and some of the OMA methods missed the fundamental lateral mode. Overall the PP method was the most successful in finding the largest number of frequencies but the SSI method yielded the highest quality mode shapes. The SSI method is, however, computationally more expensive that the remaining two methods. For quick, preliminary results, the PP and FDD methods can still be useful and detailed analyses could use SSI and FDD. Overall, the study argues that output-only system identification can provide useful quantitative insights into the modal properties of stiff bridges even under weak environmental excitations, or poorer SNRs, but its limitations need to be acknowledged. (C) 2017 Elsevier Ltd. All rights reserved.
机译:从环境振动响应中准确识别桥梁结构动态特性的挑战仍然存在,因为与理想的实验室环境不同,在任何现场测试地点都不可避免地会出现数据噪声水平升高的情况,这可能会对模态参数的识别产生不利影响过程和结果。对于在弱环境激发源下进行的振动测试会导致信噪比(SNR)较差的情况尤其如此。本文介绍了在这种弱激励条件下使用操作模式分析(OMA)技术进行模式识别的可行性和可靠性,并通过廉价的独立加速度计/数据记录仪测量了响应,从而进行了调查。一座11跨度的混凝土高速公路匝道桥因无法通行而引起的兴奋仅是由于通过该桥下面的高速公路以及附近高速公路上,下匝道上的交通,微风以及可能的微小震动所引起的地面振动而引起的震颤。桥面板上测量点的高空间分辨率用于收集振动响应。利用三种仅输出的模态参数识别算法来提取模态属性,即峰拾取(PP),频域分解(FDD)和数据驱动的随机子空间识别(SSI)方法。尽管环境激励和传感器噪声较弱,但仍可以识别出9个低于10 Hz的横向和三个垂直模态频率。确定的实验固有频率是稳定的,但是阻尼比却有明显的分散。但是,与使用有限元模型进行的数值模态分析的结果进行比较后发现,实验结果完全缺少几个更高阶的垂直模态,而某些OMA方法却缺少了基本的横向模态。总的来说,PP方法在找到最大数量的频率方面是最成功的,但是SSI方法产生了最高质量的模式形状。但是,SSI方法在计算上比其余两种方法更为昂贵。对于快速的初步结果,PP和FDD方法仍然有用,并且可以使用SSI和FDD进行详细分析。总体而言,该研究认为,即使在弱环境激励或SNR较差的情况下,纯输出系统识别也可以为刚性桥梁的模态特性提供有用的定量见解,但需要认识到其局限性。 (C)2017 Elsevier Ltd.保留所有权利。

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