Title Modal Identification Study of Vincent Thomas Bridge Using Simulated Wind-Induced Ambient Vibration Data Permalink (original) (raw)
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Computer-Aided Civil and Infrastructure Engineering, 2008
In this paper, wind-induced vibration response of Vincent Thomas Bridge, a suspension bridge located in San Pedro near Los Angeles, California, is simulated using a detailed three-dimensional finite element model of the bridge and a state-of-the-art stochastic wind excitation model. Based on the simulated wind-induced vibration data, the modal parameters (natural frequencies, damping ratios, and mode shapes) of the bridge are identified using the data-driven stochastic subspace identification method. The identified modal parameters are verified by the computed eigenproperties of the bridge model. Finally, effects of measurement noise on the system identification results are studied by adding zero-mean Gaussian white noise processes to the simulated response data. Statistical properties of the identified modal parameters are investigated under increasing level of measurement noise. The framework presented in this paper will allow to investigate the effects of various realistic damage scenarios in long-span cable-supported (suspension and cable-stayed) bridges on changes in modal identification results. Such studies are required in order to develop robust and reliable vibration-based structural health monitoring methods for this type of bridges, which is a long-term research objective of the authors.
The identification of modal parameters has been performed for the New Carquinez suspension bridge in California. By using multiple ambient vibration data sets recorded through a wind-motion monitoring system in the bridge, the baseline modal parameters were obtained in order to investigate dynamic behavior of the bridge in operating conditions. For the modal parameters identification, the data-driven stochastic subspace identification technique was implemented. For each data set, the modal parameters for structural modes were estimated by examining the estimation error between measured data and reconstructed one from the identified modes. Based on the results, variability of the identified modal parameters was also investigated.
System Identification from Ambient Vibration Measurements on a Bridge
Journal of Sound and Vibration, 1997
The cross-correlation function between two response measurements made on an ambiently excited structure is shown to have the same form as the system's impulse response function. Therefore, standard time domain curve-fitting procedures, which are typically applied to impulse response functions, can now be applied to the cross-correlation functions to estimate the resonant frequencies and modal damping of the structure. This derivation is based on the assumption that the ambient vibration source is a white noise random process. Curve-fitting cross-correlation functions to obtain modal properties offers advantages over standard procedures that identify resonant frequencies from peaks in the power spectrum and damping from the width of the power spectrum. The primary advantage is the ability to identify closely spaced modes and their associated damping. The resonant frequencies of a highway bridge that were identified by curve-fitting the cross-correlation functions, using traffic excitation as the ambient vibration source, are compared to modal properties identified by standard forced vibration testing methods. Results of this comparison showed a maximum discrepancy of 3·63 percent. Similar comparisons for the average modal damping values identified by the two methods showed a 9·82 percent difference. This experimental verification implies that the proposed method of analyzing ambient vibration data can be used to accurately assess the dynamic properties of structures in a non-intrusive manner. 7 1997 Academic Press Limited
System identification of suspension bridge from ambient vibration response
Engineering Structures, 2008
The paper addresses and evaluates the application of system identification to a suspension bridge using ambient vibration response. Toobtain dynamic characteristics of the bridge, two output-only time-domain system identification methods are employed namely, the RandomDecrement Method combined with the Ibrahim Time Domain (ITD) method and the Natural Excitation Technique (NExT) combined with theEigensystem Realization Algorithm (ERA). Accuracy and efficiency of both methods are investigated, and compared with the results from a FiniteElement Model. The results of system identification demonstrate that using both methods, ambient vibration measurement can provide reliableinformation on dynamic characteristics of the bridge. The NExT-ERA technique, however, is more practical and efficient especially when appliedto voluminous data from multi-channel measurement. The results from three days of measurements indicate the wind-velocity dependency of natural frequency and damping ratio particularly for low-order modes. The sources of these dependencies appear to be the effect of aerodynamicforces alongside the girder, and friction force from the bearing near the towers.
System Identification of Alfred Zampa Memorial Bridge Using Dynamic Field Test Data
Journal of Structural Engineering, 2009
The Alfred Zampa Memorial Bridge (AZMB), a newly built long-span suspension bridge, is located 32km northeast of San Francisco on interstate Highway I-80. A set of dynamic field tests were conducted on the AZMB in November 2003, just before the bridge opening to traffic. These tests provided a unique opportunity to identify the modal properties of the bridge in its as-built condition with no previous traffic loads or seismic excitation. A benchmark study on modal identification of the AZMB is performed using three different state-of-the-art system identification algorithms based on ambient as well as forced vibration measurements. These system identification methods consist of: (1) the multiple-reference natural excitation technique combined with the eigensystem realization algorithm, (2) the data-driven stochastic subspace identification method, and (3) the enhanced frequency domain decomposition method. Overall, the modal parameters identified using these system identification methods are found to be in very good agreement for each type of tests (ambient and forced vibration tests). For most vibration modes, the natural frequencies and mode shapes identified using the two different types of test data also match very well. However, the modal damping ratios identified from forced vibration test data are in general higher than those estimated from ambient vibration data. The identified natural frequencies and -2-mode shapes are finally compared with their analytical counterparts from a three-dimensional finite element model of the AZMB. The modal properties of the AZMB presented in this paper can be used as baseline in future health monitoring studies of this bridge.
Conference Proceedings of the Society for Experimental Mechanics Series, 2017
The Bergsøysund Bridge is currently being extensively monitored with accelerometers, anemometers, wave radars and GNSS sensors. By applying Covariance-driven Stochastic Subspace Identification (Cov-SSI), the modal parameters of the bridge are estimated. The results are interpreted in the context of the environment, represented by significant wave heights. The problem is characterized by the fact that modes are closely spaced in frequency and have high damping. Two weighting algorithms for the Cov-SSI are applied, to assess their performance for application on structures with these characteristics.
Statistical significance of modal parameters of bridge systems identified from strong motion data
Earthquake Engineering & Structural Dynamics, 2005
Modal parameters of structural systems have commonly been determined using system identiÿcation (SI) methods for damage detection and health monitoring. For determining the deterioration of the integrity of structural systems correctly, modal parameters of a healthy structure have to be obtained with adequate certainty so that these parameters can be used as reliable references for the healthy system to compare with those of the damaged system. In this study, the statistical signiÿcance of modal parameters identiÿed using strong motion time histories recorded on two bridge structures is assessed. The conÿdence intervals of identiÿed modal frequencies and damping ratios are obtained using Monte Carlo simulations and sensitivity analyses in conjunction with eigenrealization algorithm. The dependence of the statistical bounds on model parameters is examined. The e ect of using di erent number of sensors on the statistical signiÿcance is evaluated using simulated time history data from a validated ÿnite element model of a bridge.
Uncertainty of Bridge Vibration Properties and Its Consequence for Damage Identification
Proceedings of the 5th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COMPDYN 2015), 2015
This study deals with the topic of bridge health monitoring based on identification of bridge vibration properties, which are influenced by uncertainties. Focus is laid on quantification of the uncertainties from continuous monitoring data. The determined uncertainties are then used in probabilistic evaluation of structural state. The purpose is to acquire besides the "best-match" structural state also the reliability of identification. A method for automated extraction of modal parameters and evaluation of their uncertainties is presented. It comprises clustering and statistical evaluation of Stochastic Subspace Identification system poles. The statistical properties of modal parameters are used to build a set of probabilistic variables sampled using Monte-Carlo Simulation. The generated sets are used for probabilistic damage detection, which uses a forward approach of finding the best match within a database of precalculated structural states. The presented study was performed using monitoring data acquired on a prestressed concrete box-girder bridge in course of 9 months of continuous data recording.
Operational modal analysis under wind load using stochastic subspace identification
The extraction of modal parameters from a real structure represents an important step in modal analysis. When only the output signal is available in an experiment, the system identification process is referred as operation modal analysis (OMA). Applications of those cases are fond for structures where the ambient excitation (wind, traffic, waves, nearby systems, etc.) can not be removed or is the only possible one. Once the input signals can not be measured, some assumptions in their random nature are needed together with a stochastic modeling of the system. Among several methods, the stochastic subspace identification (SSI) has been shown to be a consistent one and, therefore, was chosen to be used in this paper. Here, the modal analysis of a system under wind load is studied. The fluid-structure interaction force is usually not easy to be represented and its whiteness (assumption made in most of OMA methods) can not be easily conformed. In this way, a two floor building model is used for experimental validation, where different fluid-structure interaction were created. The paper begins with a presentation of the discrete state space model followed by the SSI theory. Two popular SSI algorithms are presented: covariance-driven and data-driven. A efficient way to select the correct parameters for the method is discussed together with a procedure to analyze the results. To exemplify the identification process, experimental results are shown and the identified parameters are listed. As conclusion, the wind has been shown to be a good excitation source for OMA once the system has been correct identified.
Automated modal identification in operational conditions and its application to bridges
Engineering Structures, 2013
The increasing diffusion of long term dynamic monitoring systems for structural condition assessment is currently driving a strong interest towards automated procedures of output-only modal identification. Different approaches have been recently developed in the literature for this purpose, often based on Stochastic Subspace Identification (SSI) methods. Such procedures usually rely on heuristic decisional criteria, hence demanding for independent checks with validation purposes.