环境激励下桥梁结构模态频率与波速变化监测研究

基于环境激励的桥梁健康监测方法是未来的发展方向。我们采用宽频带地震计Guralp CMG-6TD,建立了在线桥梁监测系统,对北京市昌平区鲁疃西路大桥进行了连续监测。分别使用随机子空间识别获得桥梁模态参数和尾波干涉法监测桥梁波速变化。结果表明：（1）采用的高灵敏度宽频带速度型地震计,能够连续准确记录宽频带范围内的三分量振动信号,非常适用于长期桥梁动态监测;（2）基于连续记录的环境激励信号,随机子空间法能够稳定可靠地识别桥梁低阶模态参数,尾波干涉法能够连续精确地监测桥梁材料的细微波速变化;（3）温度变化会引起桥梁材料的弹性模量发生显著变化。波速变化呈现振幅为1%的日变化特征,与温度变化呈明显的反相关性,温度敏感系数约为。同时模态频率的变化不太明显,说明波速可能是比模态频率更为敏感的量化损伤指标;（4）模态频率是桥梁整体状态的一种度量,而波速变化可以监测桥梁局部材料弹性性质的变化。将这两者结合起来,利用环境激励信号,连续准确识别桥梁模态频率以及监测波速变化,对于桥梁健康监测具有潜在重要应用价值。     

改进曲率模态识别桥梁损伤位置方法研究

结构健康监测和结构状态评估的主要前提之一是结构损伤识别。基于曲率模态对结构局部损伤比较敏感和频率指标测试简单方便、精度高的特点,本文提出了一种以结构的曲率模态为基础,综合考虑频率的变化的改进的结构损伤识别方法。随机子空间方法是一种行之有效的基于环境激励的结构状态识别方法。该方法的主要优点是无需人工激励,不中断桥梁的运营。为此,论文提出了一种不中断桥梁运营的基于改进曲率模态的桥梁结构损伤识别方法。最后用一三跨连续梁的有限元模型对该改进方法进行了验证。结果表明,采用随机子空间结合改进的曲率模态方法可以在不中断桥梁运营的前提下有效地识别出桥梁的损伤状况。     

佛开高速九江大桥模态测试与分析

对主跨为160m的预应力砼变截面连续梁桥佛开高速九江大桥进行了理论与实验模态分析．首先介绍了环境激励条件下现场模态测试布置及过程,利用随机子空间法（SSI）进行了桥梁模态参数识别．建立了桥梁有限元模型．并对理论和实验模态分析结果进行了比较和讨论。实验与有限元计算结果在竖向模态频率及振型上总体吻合较好。测试结果可以为有限元模型修正提供依据：模态测试与有限元分析相结合．可以为桥梁长期健康监测和损伤评估提供较可靠的基准模型。     

环境激励下基于波动能量法的结构损伤识别

目前作为结构健康监测系统核心的损伤识别大多是基于模态参数变化而进行的,但模态参数对局部损坏不敏感,导致损伤识别精度不够。波在结构中的传播状态可以更好地反映局部损伤状况,波动能量可以作为损伤识别的有效指标。为了提高环境激励下结构损伤识别的精度,采用S变换分析了结构输出信号,建立波动能量指标,从而使波动能量指标的使用领域扩展到非平稳信号范围。最后通过三层钢框架试验及弹性分层剪切梁的数值模型对该方法进行了验证,结果表明:该方法不仅能够有效识别结构损伤位置,而且能够识别出损伤程度。     

利用三分量地震计检测自然载荷下桥梁结构响应特征

基于环境激励信号的桥梁结构响应在线检测系统,利用高灵敏度三分量宽频带地震计,连续监测北京市四座典型在役桥梁在自然荷载作用下的微弱振动信号,分别利用峰值法和互相关函数法获得了在役桥梁结构不同方向上的频谱特征及其结构响应特征。结果表明:(1)三分量地震计能够准确可靠地连续记录宽频带范围内的环境激励的微弱振动信号,非常适用于构建新型的桥梁结构响应检测系统;(2)峰值法和互相关函数法都能够可靠地识别多阶桥梁模态频率,互相关函数法的识别结果更为稳定;(3)桥梁的模态频率受桥梁结构、材料、环境温度等多种因素影响,桥梁不同方向的固有振动频率不同,不同类型的桥梁的结构响应也存在显著差异。该桥梁结构响应检测技术为在役桥梁实时健康诊断打下了基础。     

在线监测环境下土木结构的模态识别研究

建立在线监测环境下结构考虑时效的模态识别计算方案。采用基于各测点加速度响应互功率谱的频域多参考点模态识别法来实现结构模态参数的抽取,从而绕过了监测环境下激励监测的技术难题;并用频域的平均法使识别参数的拟合曲线平滑和发现参数的变化趋势。通过对美国结构健康监测研究小组公布的Benchmark问题的第一阶段解析模型模拟加速度响应数据的识别,表明本文采用的算法有较好的识别精度和识别速度,是一个可行的在线监测环境下的模态识别计算方案。     

随机子空间方法在桥塔模态参数识别中的应用

基于环境振动的结构模态参数识别方法正逐渐成为国内外研究的一大热点。环境振动方法就是仅仅利用结构测试的输出信号进行结构的模态参数识别,随机子空间方法就是其中的一种。随机子空间法是近年来发展起来的一种线性系统辩识方法,可以有效地从环境激励的结构响应中获取模态参数。它属于时域的方法,该方法不需要进行FFT变换,它不仅可以识别结构的频率,而且可以识别结构的阻尼和振型。文章首先介绍了随机子空间的理论,然后用该方法对正在施工中的南京长江三桥的南塔进行模态参数识别,通过与其他方法的识别结果进行比较,证明随机子空间方法不失为一种有效的模态参数识别方法。     

基于高斯曲率模态相关系数的梁桥支座损伤识别研究

为方便地检测梁桥支座损伤,提出了利用运营桥梁实测模态位移结合其无损状态的模态位移判断支座损伤的高斯曲率模态相关系数法。通过简支梁桥的室内试验,验证了利用高斯曲率模态相关系数判定支座损伤的合理性以及该方法中无损状态下的模态位移可以通过模态试验和有限元模拟两种方法获得。利用该方法对实际简支梁桥和连续梁桥进行的支座损伤识别结果表明:高斯曲率模态相关系数法可准确识别出单支座和多支座损伤的支座损伤位置,具有较强的鲁棒性,可将此方法应用于实际工程中的支座损伤识别。     

基于经验模式分解的随机子空间识别方法

提出了基于经验模式分解（EMD）的环境激励结构模态参数随机子空间识别（SSI）方法。该方法用设置间断频率的EMD将结构环境振动响应原始信号分解成若干个基本模式分量（IMF）,使每一个基本模式分量仅为结构的某一阶固有模态,进而用随机子空间方法进行模态参数识别。实桥环境振动实验分析结果表明,该方法能有效地避免结构各阶模态之间的相互影响,能够更清晰方便地得到结构的模态参数。     

Morlet 小波用于环境激励下的模态参数识别研究

本文分别从卷积和Parseval定理的角度推导了非正交小波变换系数的实用计算方法。在环境激励下以互相关函数代替系统的自由响应数据,给出了基于Morlet小波变换的频率、阻尼比的参数识别方法,重点介绍了基于最小二乘法的振型识别技术。采用2层楼仿真算例和潮白河桥应用实例验证本算法,识别结果表明基于Morlet小波变换的模态参数识别技术能够有效地识别出环境激励下系统的模态参数。     

基于摄动有限元方法对梁结构损伤的识别

结构损伤的定量识别是工程技术中急待解决的问题。利用矩阵摄动和结构有限元动力学理论推出梁结构损伤程度定量识别的公式和方法,该方法仅需要在役结构的固有频率测量值就可识别结构的损伤位置和损伤程度,而且可以识别结构的老化程度,避免了由模态振型识别损伤,因测量自由度不足带来的误差,通过对一钢悬臂梁损伤识别的数值仿真,证明了该方法的有效性。该方法具有较大的工程应用价值。     

基于损伤部位向量法的钢框架损伤识别研究

对损伤部位向量(DLV)法作了简单介绍,并用该方法对钢框架进行了损伤识别和损伤定位。该方法假定结构损伤前后为线性,对结构损伤前后柔度矩阵差进行奇异值分解,将奇异值为零所对应的向量,作为静荷载施加在无损结构的测点位置,则应力为零的单元为可能损伤的单元。对3种不同工况的钢框架进行了振动模态试验,用前3阶模态参数构造框架的柔度矩阵,按照DLV法对其进行了损伤识别,识别结果与已知损伤情况相一致。从测试自由度不完备、噪声和振型质量归一化系数这3个方面对识别效果进行了分析,结果表明:当损伤使结构动力特性有微小改变时,使用该方法不易定位损伤,应结合局部损伤识别方法进行判定;当损伤使结构动力特性有较大改变时,该方法能有效识别损伤的单元。DLV方法概念简单,理论明确,不受结构类型的限制,不需要结构的数学模型和模型缩聚或扩展技术,只需获得结构损伤前后的前几个低阶模态参数,即可识别结构一处或多处损伤,实际应用时可操作性强。     

混凝土框架模型结构参数的识别

框架结构是常见的建筑结构形式,也是结构损伤诊断的主要研究对象之一。本文以钢筋混凝土框架结构模型的振动测试数据为基础,采用灵敏度分析方法,对结构的物理参数进行识别。有限元分析中,考虑框架结构的节点转动,采用静凝聚方法得到结构刚度矩阵。参数识别结果表明,对于不同的固有频率和振型的测试信息组合所识别的物理参数有所不同。根据已知的概率分布,利用MonteCarlo方法,将模态参数的不确定性传递给物理参数,得到了物理参数的不确定性。     

Structural modal parameter identification and damage diagnosis based on Hilbert-Huang transform

Traditional modal parameter identifi cation methods have many disadvantages,especially when used for processing nonlinear and non-stationary signals.In addition,they are usually not able to accurately identify the damping ratio and damage.In this study,methods based on the Hilbert-Huang transform(HHT) are investigated for structural modal parameter identifi cation and damage diagnosis.First,mirror extension and prediction via a radial basis function(RBF) neural network are used to restrain the troublesome end-effect issue in empirical mode decomposition(EMD),which is a crucial part of HHT.Then,the approaches based on HHT combined with other techniques,such as the random decrement technique(RDT),natural excitation technique(NExT) and stochastic subspace identifi cation(SSI),are proposed to identify modal parameters of structures.Furthermore,a damage diagnosis method based on the HHT is also proposed.Time-varying instantaneous frequency and instantaneous energy are used to identify the damage evolution of the structure.The relative amplitude of the Hilbert marginal spectrum is used to identify the damage location of the structure.Finally,acceleration records at gauge points from shaking table testing of a 12-story reinforced concrete frame model are taken to validate the proposed approaches.The results show that the proposed approaches based on HHT for modal parameter identifi cation and damage diagnosis are reliable and practical.     

Evolution of modal characteristics of a mid-rise cold-formed steel building during construction and earthquake testing

Vibration-based structural identification is an essential technique for assessing structural conditions by inferring information from the dynamic characteristics of structures. However, the robustness of such techniques in monitoring the progressive damage of real structures has been validated with only a handful of research efforts, largely due to the paucity of monitoring data recorded from damaged structures. In a recent experimental program, a mid-rise cold-formed steel building was constructed at full scale atop a large shake table and subsequently subjected to a unique multi-hazard scenario including earthquake, post-earthquake fire, and finally post-fire earthquake loading. Complementing the simulated hazard events, low-amplitude vibration tests, including ambient vibrations and white noise base excitation tests, were conducted throughout the construction and the test phases. Using the vibration data collected during the multi-hazard test program, this paper focuses on understanding the modal characteristics of the cold-formed steel building in correlation with the construction and the structural damage progressively induced by the simulated hazard events. The modal parameters of the building (i.e., natural frequencies, damping ratios, and mode shapes) are estimated using two input–output and two output-only time-domain system identification techniques. Agreement between the evolution of modal parameters and the observations of the progression of physical damage demonstrates the effectiveness of the vibration-based system identification techniques for structural condition monitoring and damage assessment.     

System identification of linear structures based on Hilbert–Huang spectral analysis. Part 1: normal modes

Based on the Hilbert–Huang spectral analysis, a method is proposed to identify multi‐degree‐of‐freedom (MDOF) linear systems using measured free vibration time histories. For MDOF systems, the normal modes have been assumed to exist. In this method, the measured response data, which are polluted by noises, are first decomposed into modal responses using the empirical mode decomposition (EMD) approach with intermittency criteria. Then, the Hilbert transform is applied to each modal response to obtain the instantaneous amplitude and phase angle time histories. A linear least‐square fit procedure is proposed to identify the natural frequency and damping ratio from the instantaneous amplitude and phase angle for each modal response. Based on a single measurement of the free vibration time history at one appropriate location, natural frequencies and damping ratios can be identified. When the responses at all degrees of freedom are measured, the mode shapes and the physical mass, damping and stiffness matrices of the structure can be determined. The applications of the proposed method are illustrated using three linear systems with different dynamic characteristics. Numerical simulation results demonstrate that the proposed system identification method yields quite accurate results, and it offers a new and effective tool for the system identification of linear structures in which normal modes exist. Copyright © 2003 John Wiley & Sons, Ltd.     

两种识别结构模态参数时域法的对比研究

模态参数是有效评估结构安全状况的关键参数,在结构抗震加固和健康诊断领域得到广泛应用。与频域法相比较,时域法直接利用实测的振动信号识别模态参数,不需要进行频域变换,减少数据处理带来的误差,并且可以实现大型结构的在线识别,真实地反应结构的现状。以同济大学12层钢筋混凝土标准框架振动台模型试验完整数据为对象,在详细介绍ITD法和复指数法2种时域法理论的基础上,通过编程选取结构不同测点的振动加速度时程数据,识别了小震和强震工况下12层钢筋混凝土框架模型振动台试验模型的模态频率和阻尼比,并结合移动谱识别结构模态参数的时变特性。结果表明:ITD法和复指数法可有效地识别结构的模态参数,自振频率的识别精度较高,而阻尼比的离散度较大;小震工况频率变化值不大,而强震工况频率值较初始时刻有明显的下降,这与试验现象是吻合的,进一步说明移动谱与这2种时域法相结合可以反应结构在塑性阶段的参数时变特性。     

Damage assessment through structural identification of a three‐story large‐scale precast concrete structure

This paper investigates the damage assessment of a three‐story half‐scale precast concrete building resembling a parking garage through structural identification. The structure was tested under earthquake‐type loading on the NEES large high‐performance outdoor shake table at the University of California San Diego in 2008. The tests provide a unique opportunity to capture the dynamic performance of precast concrete structures built under realistic boundary conditions. The effective modal parameters of the structure at different damage states have been identified from white‐noise and scaled earthquake test data with the assumption that the structure responded in a quasi‐linear manner. Modal identification has been performed using the deterministic‐stochastic subspace identification method based on the measured input–output data. The changes in the identified modal parameters are correlated to the observed damage. In general, the natural frequencies decrease, and the damping ratios increase as the structure is exposed to larger base excitations, indicating loss of stiffness, development/propagation of cracks, and failure in joint connections. The analysis of the modal rotations and curvatures allowed the localization of shear and flexural damages respectively and the checking of the effectiveness of repair actions. Copyright © 2013 John Wiley & Sons, Ltd.     

Two‐stage damage detection algorithms of structure using modal parameters identified from recursive subspace identification

Structural damage assessment under external loading, such as earthquake excitation, is an important issue in structural safety evaluation. In this regard, appropriate data analysis and feature extraction techniques are required to interpret the measured data and to identify the state of the structure and, if possible, to detect the damage. In this study, the recursive subspace identification with Bona‐fide LQ renewing algorithm (RSI‐BonaFide‐Oblique) incorporated with moving window technique is utilized to identify modal parameters such as natural frequencies, damping ratios, and mode shapes at each instant of time during the strong earthquake excitation. From which the least square stiffness method (LSSM) combined with the model updating technique, called efficient model correction method (EMCM), is used to estimate the first‐stage system stiffness matrix using the simplified model from the previously identified modal parameters (nominal model). In the second stage, 2 different damage assessment algorithms related to the nominal system stiffness matrix were derived. First, the model updating technique, called EMCM, is applied to correct the nominal model by the newly identified modal parameters during the strong motion. Second, the element damage index can be calculated using element damage index method (EDIM) to quantify the damage extent in each element. Verification of the proposed methods through the shaking table test data of 2 different types of structures and a building earthquake response data is demonstrated to specify its corresponding damage location, the time of occurrence during the excitation, and the percentage of stiffness reduction.