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。     

Blind modal identification of output‐only structures in time‐domain based on complexity pursuit

Output‐only modal identification is needed when only structural responses are available. As a powerful unsupervised learning algorithm, blind source separation (BSS) technique is able to recover the hidden sources and the unknown mixing process using only the observed mixtures. This paper proposes a new time‐domain output‐only modal identification method based on a novel BSS learning algorithm, complexity pursuit (CP). The proposed concept—independent ‘physical systems’ living on the modal coordinates—connects the targeted constituent sources (and their mixing process) targeted by the CP learning rule and the modal responses (and the mode matrix), which can then be directly extracted by the CP algorithm from the measured free or ambient system responses. Numerical simulation results show that the CP method realizes accurate and robust modal identification even in the closely spaced mode and the highly damped mode cases subject to non‐stationary ambient excitation and provides excellent approximation to the non‐diagonalizable highly damped (complex) modes. Experimental and real‐world seismic‐excited structure examples are also presented to demonstrate its capability of blindly extracting modal information from system responses. The proposed CP is shown to yield clear physical interpretation in modal identification; it is computational efficient, user‐friendly, and automatic, requiring little expertise interactions for implementations. 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.     

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.     

A reassessment of dynamic characteristics of the Quincy Bayview Bridge using output‐only identification techniques

A reassessment of the dynamic characteristics of the 542 m cable‐stayed Bayview Bridge in Quincy, Illinois, is presented using a newly developed output‐only system identification technique. The technique is applied to an extensive set of ambient vibration response data acquired from the bridge in 1987. Vertical, torsional and transverse modal frequencies of the deck are identified, and uncertainty in damping values are estimated using an automated procedure on several redundant measurements at four locations. Important practical implementation issues associated with the implementation of the procedure and selection of algorithm design parameters for stochastic subspace identification techniques are discussed. An overall mean and standard deviation of damping of 1.0±0.8% is estimated considering all identified vertical, torsional and transverse modes in the 0–2 Hz band. The mean damping for the fundamental vertical mode (0.37 Hz) is identified as 1.4±0.5%, and for the first coupled torsion–transverse mode (0.56 Hz) is identified as 1.1±0.8%. Variability in the damping estimates is shown to decrease as estimated modal RMS acceleration levels increase. Standard deviations on estimated damping range from 0.05% to 2%. The results are shown to be a substantial improvement in the evaluation of damping compared to earlier spectral analysis conducted on the same data set. Copyright © 2005 John Wiley & Sons, Ltd.     

Probabilistic approach for modal identification using non‐stationary noisy response measurements only

This paper addresses the problem of identification of the modal parameters for a structural system using measured non‐stationary response time histories only. A Bayesian time‐domain approach is presented which is based on an approximation of the probability distribution of the response to a non‐stationary stochastic excitation. It allows one to obtain not only the most probable values of the updated modal parameters and stochastic excitation parameters but also their associated uncertainties using only one set of response data. It is found that the updated probability distribution can be well approximated by a Gaussian distribution centred at the most probable values of the parameters. Examples using simulated data are presented to illustrate the proposed method. Copyright © 2002 John Wiley & Sons, Ltd.     

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.     

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.     

Correlating dynamic characteristics from field measurements and numerical analysis of a high‐rise building

Using the concept of lumped masses and rigid floor slabs, several mathematical models were built using a popular PC‐based finite element program to model a tall building with a frame‐core wall structural system. These models were analysed to obtain the first nine mode shapes and their natural frequencies which were compared with those from field measurements, using numerical correlation indicators. The comparison shows several factors that can have a significant effect on the analysis results. Firstly, outriggers connecting the outer framed tube system to the inner core walled tube system have a significant effect on fundamental translational mode behaviour. Secondly, detailed modelling of the core considering major and minor openings as well as internal thin walls has the strongest influence on torsional behaviour, whose measurements were shown to be an important aspect of the dynamic behaviour for the structure studied. Fine tuning of an analytical model requires not just considering variation in values of structural parameters but also attention to fine detail. Copyright © 2000 John Wiley & Sons, Ltd.     

Output‐only structural identification in time domain: Numerical and experimental studies

By identifying changes in stiffness parameters, structural damage can be detected and monitored. Although considerable progress has been made in this research area, many challenges remain in achieving robust structural identification based on incomplete and noisy measurement signals. The identification task is made even more difficult if measurement of input force is to be eliminated. To this end, an output‐only structural identification strategy is proposed to identify unknown stiffness and damping parameters. A non‐classical approach based on genetic algorithms (GAs) is adopted. The proposed strategy makes use of the recently developed GA‐based method of search space reduction, which has shown to be able to accurately and reliably identify structural parameters from measured input and output signals. By modifying the numerical integration scheme, input can be computed as the parameter identification task is in progress, thereby eliminating the need to measure forces. Numerical and experimental results demonstrate the power of the strategy in accurate and efficient identification of structural parameters and damage using only incomplete acceleration measurements. Copyright © 2007 John Wiley & Sons, Ltd.     

Substructural first‐ and second‐order model identification for structural damage assessment

This paper presents two methods to perform system identification at the substructural level, taking advantage of reduction in the number of unknowns and degrees of freedom (DOFs) involved, for damage assessment of fairly large structures. The first method is based on first‐order state space formulation of the substructure where the eigensystem realization algorithm (ERA) and the observer/Kalman filter identification (OKID) are used. Identification at the global level is then performed to obtain the second‐order model parameters. In the second method, identification is performed at the substructural level in both the first‐ and second‐order model identification. Both methods are illustrated using numerical simulation studies where results indicate their significantly better performance than identification using the global structure, in terms of efficiency and accuracy. A 12‐DOF system and a fairly large structural system with 50 DOFs are used where the effects of noisy data are considered. In addition to numerical simulation studies, laboratory experiments involving an eight‐storey frame model are carried out to illustrate the performance of the proposed method. The identification results presented in terms of the stiffness integrity index show that the proposed methodology is able to locate and quantify damage fairly accurately. Copyright © 2005 John Wiley & Sons, Ltd.     

A modal pushover analysis procedure to estimate seismic demands for unsymmetric‐plan buildings

An Erratum has been published for this article in Earthquake Engng. Struct. Dyn. 2004; 33:1429. Based on structural dynamics theory, the modal pushover analysis (MPA) procedure retains the conceptual simplicity of current procedures with invariant force distribution, now common in structural engineering practice. The MPA procedure for estimating seismic demands is extended to unsymmetric‐plan buildings. In the MPA procedure, the seismic demand due to individual terms in the modal expansion of the effective earthquake forces is determined by non‐linear static analysis using the inertia force distribution for each mode, which for unsymmetric buildings includes two lateral forces and torque at each floor level. These ‘modal’ demands due to the first few terms of the modal expansion are then combined by the CQC rule to obtain an estimate of the total seismic demand for inelastic systems. When applied to elastic systems, the MPA procedure is equivalent to standard response spectrum analysis (RSA). The MPA estimates of seismic demand for torsionally‐stiff and torsionally‐flexible unsymmetric systems are shown to be similarly accurate as they are for the symmetric building; however, the results deteriorate for a torsionally‐similarly‐stiff unsymmetric‐plan system and the ground motion considered because (a) elastic modes are strongly coupled, and (b) roof displacement is underestimated by the CQC modal combination rule (which would also limit accuracy of RSA for linearly elastic systems). Copyright © 2004 John Wiley & Sons, Ltd.     

Subspace system identification of support excited structures—part II: gray‐box interpretations and damage detection

A theoretical framework is presented for the estimation of the physical parameters of a structure (i.e., mass, stiffness, and damping) from measured experimental data (i.e., input–output or output‐only data). The framework considers two state‐space models: a physics‐based model derived from first principles (i.e., white‐box model) and a data‐driven mathematical model derived by subspace system identification (i.e., black‐box model). Observability canonical form conversion is introduced as a powerful means to convert the data‐driven mathematical model into a physically interpretable model that is termed a gray‐box model. Through an explicit linking of the white‐box and gray‐box model forms, the physical parameters of the structural system can be extracted from the gray‐box model in the form of a finite element discretization. Prior to experimental verification, the framework is numerically verified for a multi‐DOF shear building structure. Without a priori knowledge of the structure, mass, stiffness, and damping properties are accurately estimated. Then, experimental verification of the framework is conducted using a six‐story steel frame structure under support excitation. With a priori knowledge of the lumped mass matrix, the spatial distribution of structural stiffness and damping is estimated. With an accurate estimation of the physical parameters of the structure, the gray‐box model is shown to be capable of providing the basis for damage detection. With the use of the experimental structure, the gray‐box model is used to reliably estimate changes in structural stiffness attributed to intentional damage introduced. Copyright © 2012 John Wiley & Sons, Ltd.     

The application of Karhunen–Loéve,or principal component analysis method,to study the non‐linear seismic response of structures

The Karhunen–Loéve (K–L) method is used to interpret dynamic response data obtained from shaking table and pseudodynamic tests conducted on civil engineering structures subjected to earthquake loading. It is shown how the K–L method can be used to monitor on‐line, or a posteriori, the structural response of non‐linear dynamical systems. Results from these analyses make it possible to quantitatively verify the number and participation factors of non‐linear modes and how they correspond to physical behaviour of the structure. Comments are made regarding the use of this technique in various fields including numerical calculations, experiments and control. Copyright © 2000 John Wiley & Sons, Ltd.     

Non‐parametric–parametric model for random uncertainties in non‐linear structural dynamics: application to earthquake engineering

This paper deals with the transient response of a non‐linear dynamical system with random uncertainties. The non‐parametric probabilistic model of random uncertainties recently published and extended to non‐linear dynamical system analysis is used in order to model random uncertainties related to the linear part of the finite element model. The non‐linearities are due to restoring forces whose parameters are uncertain and are modeled by the parametric approach. Jayne's maximum entropy principle with the constraints defined by the available information allows the probabilistic model of such random variables to be constructed. Therefore, a non‐parametric–parametric formulation is developed in order to model all the sources of uncertainties in such a non‐linear dynamical system. Finally, a numerical application for earthquake engineering analysis is proposed concerning a reactor cooling system under seismic loads. Copyright © 2003 John Wiley & Sons, Ltd.     

Discrete principal‐monotonicity inference for hydro‐system analysis under irregular nonlinearities,data uncertainties,and multivariate dependencies. Part II: Application to streamflow simulation in the Xingshan Watershed,China

Provision of reliable scientific support to socio‐economic development and eco‐environmental conservation is challenged by complexities of irregular nonlinearities, data uncertainties, and multivariate dependencies of hydrological systems in the Three Gorges Reservoir (TGR) region, China. Among them, the irregular nonlinearities mainly represent unreliability of regular functions for robust simulation of highly complicated relationships between variables. Based on the proposed discrete principal‐monotonicity inference (DPMI) approach, streamflow generation in the Xingshan Watershed, a representative watershed in this region, is examined. Based on system characterization, predictor identification, and streamflow distribution transformation, DPMI parameters are calibrated through a two‐stage strategy. Results indicate that the modelling efficiency of DPMI is satisfactory for streamflow simulation under these complexities. The distribution transformation method and the two‐stage calibration strategy can deal with non‐normality of streamflow and temporally unstable accuracy of hydrological models, respectively. The DPMI process and results reveal that both streamflow uncertainty and its rising tendency increase with flow levels. The dominant driving forces of streamflow generation are daily lowest temperature and daily cumulative precipitation in consideration of performances in global and local scales. The temporal heterogeneity of local significances to streamflow is insignificant for meteorological conditions. There is significant nonlinearity between meteorological conditions and streamflow and dependencies among meteorological conditions. The generation mechanism of low flows is more complicated than medium flows and high flows. The DPMI approach can facilitate improving robustness of hydro‐system analysis studies in the Xingshan Watershed or the TGR region. Copyright © 2016 John Wiley & Sons, Ltd.