钢筋混凝土框架结构参数时变特性的研究

以某12层钢筋混凝土标准框架结构振动台模型试验的数据为基础,用卡尔曼滤波技术与移动谱相结合的方法对该标准框架结构参数的时变特性进行研究,该方法相当于对时域信号施加变长度矩形窗后进行卡尔曼滤波,有效地提高了参数识别精度.模型结构在小震激励时,结构的频率发生微小程度的降低,表明结构内部发生了未表现出来的轻微损伤;大震激励时,结构频率较初始时刻发生大幅度的下降,表明结构发生了较大程度的破坏.经与理论解和分量分析识别结果对比,用本文方法识别出的结构参数更接近理论解.     

一种迭代算法在结构物理参数识别中的应用

本文在利用复合反演方法进行剪切型结构物理参数识别过程中,引入一种迭代算法来求解关于参数的病态方程组,探讨了几种比例阻尼形式的比例系数识别的处理方法。以10层剪切型框架结构为例,利用该迭代算法研究了无阻尼、三种比例阻尼形式以及噪信比为5％、10％的高斯白噪声等情形下的参数识别问题。结果表明,该迭代算法能很好地求解参数的病态方程组,且对四种阻尼形式和两种噪信比的噪声有很好的适应性。     

ARX结构模态参数识别方法对比(I)——基于理论地震反应时程的对比

结构参数识别是结构抗震安全性能鉴定和健康诊断的基础。利用地震观测记录来识别结构模态参数,是地震工程领域备受关注的研究课题之一。本文利用三层平面框架结构的理论地震反应时程,对一维、多维和整体ARX模型三种模态参数识别方法进行了对比分析。结果表明：整体ARX模型的识别结果较为稳定且精度较高,更适合于进行多自由度结构参数识别。     

基于高阶局部模态的弹性地基上框架结构物理参数识别研究

利用框架结构的整体振动模态信息进行局部损伤的判别具有明显的局限性。高阶模态逐渐被人们认识并用来进行局部物理参数识别并用来进行损伤判别。本文以弹性地基上独立基础的框架结构底层柱为研究对象,利用增加的质量块对柱子进行局部损伤的制造,利用脉冲锤击法和激振器扫频实验进行高阶模态对比测试,利用PolyMAX模态分析方法进行损伤前后高阶模态的识别,发现了“高灵敏度高阶模态”的存在。最后通过两端约束Euler梁的计算模型,通过高阶模态来识别物理参数以及地基参数,其中物理参数结果具有较好的可靠性。     

基于有限测点模态信息的结构物理参数识别

研究了测试信息不完备情况下的结构参数识别问题,针对稀疏模态的结构系统,提出了基于有限测点模态信息的优化识别算法。该算法通过有限测点上的模态参数构造关于结构物理参数的目标函数,然后采用遗传算法进行参数识别。最后一数值算例说明了该算法的可行性。     

STFT变换在高层框架结构地震损伤程度识别中的应用

为了研究地震作用下高层框架结构的损伤程度信息,以结构刚度折减率为损伤程度指标,以结构的频率变化率为损伤程度识别参数,采用Matlab模拟结构在不同的损伤程度指标下的加速度响应数据,利用短时傅里叶变换方法对响应数据分析得到结构的模态参数,从而建立损伤程度指标与结构模态参数的函数关系。将损伤结构的模态参数代入函数关系式计算结构的损伤程度指标。采用同济大学振动台试验数据,利用此方法识别结构的损伤程度与振动台试验观察到的损伤程度高度吻合。     

框架结构的输入地震动记录调幅方法试验研究

为了确定框架结构弹塑性时程分析时输入地震动记录的调幅方法,本文利用一个1/40比例的框架结构振动台试验研究了四种地震动记录调幅方法的适用性。利用四种地震动记录调幅方法对20条地震动记录调幅,通过振动台依次输入不同周期的框架结构中,通过分析结构顶层的层间位移角和最大加速度两个参数的变异系数,确定最适宜此种结构体系的地震动记录调幅方法。研究表明,当结构的基本周期可以准确确定时,利用S_a(T_1)调幅方法得到的地震动记录引起结构响应的离散程度最小;当结构的基本周期不确定时,利用MIV调幅方法得到的地震动记录引起结构响应的离散程度最小;当周期有30%的变化时,ASCE调幅方法引起结构的最大加速度的变异系数波动不大,相对比较稳定;无论何时PGA调幅方法得到的变异系数都是最大,表明此法得到的离散程度最大。建议在对框架结构进行时程分析时,对于确定性周期的结构采用S_a(T_1)调幅方法,对于不确定性周期的结构采用MIV调幅方法,ASCE和PGA调幅方法不宜采用。     

基于区间分析的不确定性参数识别方法

提出一种基于区间分析的不确定性参数识别方法。该方法首先通过计算所定义的区间灵敏度指标来确定各个参数对结构动力不确定性的影响程度,经比较分析可将对动力不确定性影响较大的参数确定为不确定性参数。然后在参数选择的基础上,采用有界区间数来描述不确定性参数并进行区间响应面建模,利用响应上下限构造目标函数并按照区间序数关系,将不确定性优化目标和约束条件转化为确定性表达式,通过优化反演即可识别不确定性参数的变异性区间。最后以钢板的材料参数区间识别为例验证所提方法的可行性及有效性。参数识别结果表明所提方法在参数较多的情况下仍可准确地识别不确定性参数的变异性区间,具有较高的计算效率且可有效地避免区间运算存在的误差问题。     

基于物理参数辨识的结构损伤识别方法研究

结构损伤识别是土木工程领域的一个热门研究课题,在近几十年的发展过程中,人们一直在寻找适用于结构整体损伤的识别方法,其基本思路是利用结构的动力响应来辨识结构的力学特征参数,然后再根据这些力学特征参数的变化情况来识别损伤.本文在分析各种力学特征参数对结构损伤的适用性之后,认为结构的物理参数(刚度参数)是具有普遍适用性的损伤敏感参数,并基于此观点深入研究了物理参数辨识的相关理论和方法.     

基于地震动参数的RC框架结构易损性分析

以某典型的12层钢筋混凝土框架结构作为研究对象,研究基于非线性动力时程分析和地震动参数的RC框架结构易损性分析方法。首先采用静力pushover分析判定结构薄弱层,并确定结构性能(capacity)参数;然后应用非线性动力时程分析估计结构地震反应,研究以峰值加速度和基本周期加速度反应谱作为地震动参数结构反应的不确定性,并进一步分析结构地震需求(demand)参数与地震动参数的关系;在此基础上,分别建立该结构基于峰值加速度和加速度反应谱的易损性曲线,通过考虑场地条件对地震动特性的影响,研究场地条件对结构易损性的影响,结果表明不同场地条件下的结构易损性曲线有一定差异。应用本文方法,根据新一代地震区划图或地震安全性评价确定的地震动参数,可以直接估计结构在未来地震中出现不同破坏的概率,这在结构的抗震性能评估和地震损失预测中有一定意义。     

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。     

工作状态下梁及桥梁模态与损伤识别的新方法

工作状态下桥梁结构的模态参数识别是桥梁损伤识别的重要环节,考虑桥梁检测的实用性,桥梁检测一般应建立在环境激励的基础上,已有的环境激励下模态参数识别的方法对模态频率的识别的精度较高,而对位移模态的识别则误差较大。提出了一种利用移动质量块在不同位置时对桥梁的模态频率进行多次测量,用各次测得的频率值确定位移模态的新方法,使得位移模态识别的精度接近频率识别的精度,建立了该方法的初步模型,推导了频率与位移模态关系的理论公式,并通过数值模拟对该方法的有效性进行了说明。     

Damage identification of a 3D full scale steel–concrete composite structure with partial‐strength joints at different pseudo‐dynamic load levels

Partial‐strength composite steel–concrete moment‐resisting (MR) frame structures represent an open research field in seismic design from both a theoretical and an experimental standpoint. Among experimental techniques, vibration testing is a well‐known and powerful technique for damage detection, localization and quantification, where actual modal parameters of a structure at different states can be determined from test data by using system identification methods. However, the identification of semi‐rigid connections in framed structures is limited, and hence this paper focuses on a series of vibration experiments that were carried out on a realistic MR frame structure, following the application of pseudo‐dynamic and quasi‐static cyclic loadings at the European laboratory for structural assessment of the Joint Research Centre at Ispra, Italy, with the scope of understanding the structural behaviour and identifying changes in the dynamic response. From the forced vibration response, natural frequencies, damping ratios, modal displacements and rotations were extracted using the circle fitting technique. These modal parameters were used for local and global damage identification by updating a 3D finite element model of the intact structure. The identified results were then correlated with observations performed on the structure to understand further the underlying damage mechanisms. Finally, the latin hypercube sampling technique, a variant of the Monte Carlo method, was employed in order to study the sensitivity of the updated parameters of the 3D model to noise on the modal inputs. Copyright © 2009 John Wiley & Sons, Ltd.     

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。     

Subspace system identification of support‐excited structures—part I: theory and black‐box system identification

This paper reviews the theoretical principles of subspace system identification as applied to the problem of estimating black‐box state‐space models of support‐excited structures (e.g., structures exposed to earthquakes). The work distinguishes itself from past studies by providing readers with a powerful geometric interpretation of subspace operations that relates directly to theoretical structural dynamics. To validate the performance of subspace system identification, a series of experiments are conducted on a multistory steel frame structure exposed to moderate seismic ground motions; structural response data is used off‐line to estimate black‐box state‐space models. Ground motions and structural response measurements are used by the subspace system identification method to derive a complete input–output state‐space model of the steel frame system. The modal parameters of the structure are extracted from the estimated input–output state‐space model. With the use of only structural response data, output‐only state‐space models of the system are also estimated by subspace system identification. The paper concludes with a comparison study of the modal parameters extracted from the input–output and output‐only state‐space models in order to quantify the uncertainties present in modal parameters extracted from output‐only models. Copyright © 2012 John Wiley & Sons, Ltd.     

Output-only identification of the modal and physical properties of structures using free vibration response

The viability of a complete structural characterization of civil structures is explored and discussed. In particular, the identification of modal (i.e. natural frequencies, damping ratios and modal shapes) and physical properties (i.e. mass and stiffness) using only the structure’s free decay response is studied. To accomplish this, modal analysis from free vibration response only (MAFVRO) and mass modification (MM) methodologies are engaged along with Wavelet based techniques for optimal signal processing and modal reconstruction. The methodologies are evaluated using simulated and experimental data. The simulated data are extracted from a simple elastic model of a 5 story shear building and from a more realistic nonlinear model of a RC frame structure. The experimental data are gathered from shake table test of a 2-story scaled shear building. Guidelines for the reconstruction procedure from the data are proposed as the quality of the identified properties is shown to be governed by adequate selection of the frequency bands and optimal modal shape reconstruction. Moreover, in cases where the structure has undergone damage, the proposed identification scheme can also be applied for preliminary assessment of structural health.     

基于两阶段稳定图的随机子空间识别结构模态参数

基于振动的结构健康监测的前提是从振动测试信号中提取结构模态参数。随机子空间方法是近年来发展起来的一种线性系统辨识方法,可以有效地从环境激励的结构响应信号中提取结构模态参数。在随机子空间识别方法中,确定系统的阶数是该方法的关键工作,稳定图方法是一种比较新颖的确定系统阶次的方法。但是随机子空间方法容易产生虚假模态,这也是随机子空间方法的一个主要缺陷。因此针对于这一缺陷提出了一种基于两阶段稳定图的随机子空间识别结构模态参数方法,该方法的基本思想是将在现场采集的结构的输出信号进行分段,将各段信号用随机子空间结合稳定图进行识别,然后将所有各段所识别的模态参数再一次用稳定图方法进行分析,得出结构的模态参数。最后用一三跨连续梁的数值模型对该方法进行验证,结果表明该方法具有良好的识别效果。     

Discussion of user-defined parameters for recursive subspace identification: Application to seismic response of building structures

Structural damage assessment under external loading, such as earthquake excitation, is an important issue in structural safety evaluation. In this regard, an appropriate data analysis and system identification technique is required to interpret the measured data and to identify the state of the structure. Generally, the recursive system identification algorithm is used. In this study, the recursive subspace identification (RSI) algorithm based on the matrix inversion lemma algorithm with oblique projection technique (RSI-Inversion-Oblique) is applied to investigate the time-varying dynamic characteristics. The user-defined parameters used in the RSI-Inversion-Oblique technique are carefully discussed, which include the size of the data Hankel matrix (i), model order to extract the physical modes, and forgetting factor (FF) to detect the time-varying system modal frequencies. Response data from the Northridge earthquake from the Sherman Oaks building (CSMIP) is used as an example to examine a systematic method to determine the suitable user-defined parameters in RSI. It is concluded that the number of rows in the data Hankel matrix significantly influences the identification of the time-varying fundamental modal frequency of the structure. An algorithmic model order selection method using the eigenvalue distribution of RSI-Inversion can detect the system modal frequencies at each appending data window without causing any abnormality.     

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.     

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.