For reliable and practical application of structural health monitoring approaches in conjunction with dense sensor arrays deployed on 'smart' systems, there is a need to develop and evaluate alternate strategies for efficient problem decomposition to rapidly and accurately determine the occurrence, location and level of small changes in the underlying structural characteristics of a monitored system based on its vibrational signature. Furthermore, there is also a need to quantify the level of uncertainties in the identified system characteristics so as to have a measurable level of confidence in the parameters to be relied on for the detection of genuine changes (damage) in the monitored system. This study presents the results of two time-domain identification techniques applied to a full-scale 17-story building, based on ambient vibration measurements. The Factor building is a steel frame structure located on the UCLA campus. This building was instrumented permanently with a dense array of 72-channel accelerometers, and the acceleration data are being continuously recorded. The first identification method used in this study is the NExT/ERA, which is regarded as a global (or centralized) approach, since it deals with the global dynamic properties of the structure. The second method is a time-domain identification technique for chain-like MDOF systems. Since in this method the identification of each link of the chain is performed independently, it is regarded as a local (or decentralized) identification methodology. For the same reason, this method can be easily adopted for large-scale sensor network architectures in which the centralized approaches are not feasible due to massive storage, power, bandwidth and computational requirements. To have a statistically meaningful results, 50 days of recorded data are considered in this study. The modal parameter and chain identification procedures are performed over time windows of 2 h each and with 50% overlap. Using the NExT/ERA method, 12 dominant modes of the building were identified. It was observed that variations in the frequency estimation are relatively small; the coefficient of variation is about 1-2% for most of the estimated modal frequencies. Chain system identification was successfully implemented using the output-only data acquired from the Factor building. Probability distributions of the estimated coefficients of displacement and velocity terms in the interstory restoring functions (which are the mass-normalized local stiffness and damping values) that were found based on the chain system identification are presented. The variability of the estimated parameters due to temperature fluctuations is investigated. It is shown that there is a strong correlation between the modal frequency variations and the temperature variations in a 24 h period.

The performance of particle dampers whose behavior under broadband excitations involves internal friction and momentum transfer is a highly complex nonlinear process that is not amenable to exact analytical solutions. While numerous analytical and experimental studies have been conducted over many years to develop strategies for modeling and controlling the behavior of this class of vibration dampers, no guidelines currently exist for determining optimum strategies for maximizing the performance of particle dampers, whether in a single unit or in arrays of dampers, under random excitation. This paper focuses on the development and evaluation of practical design strategies for maximizing the damping efficiency of multi-unit particle dampers under random excitation, both the stationary and nonstationary type. High-fidelity simulation studies are conducted with a variable number of multi-unit dampers ranging from 1 to 100, with the magnitude of the “dead-space” nonlinearity being a random variable with a prescribed probability distribution spanning a feasible range of parameters. Results of the computational studies are calibrated with carefully conducted experiments with single-unit/single-particle, single-unit/multi-particle, and multiple-unit/multi-particle dampers. It is shown that a wide latitude exists in the trade-off between high vibration attenuation over a narrow range of damper gap size versus slightly reduced attenuation over a much broader range. The optimum configuration can be achieved through the use of multiple particle dampers designed in accordance with the procedure presented in the paper. A semi-active algorithm is introduced to improve the rms level reduction, as well as the peak response reduction. The utility of the approach is demonstrated through numerical simulation studies involving broadband stationary random excitations, as well as highly nonstationary excitations resembling typical earthquake ground motions.

While numerous studies have been published concerning the application of a variety of system identification techniques in conjunction with vibration measurements from civil infrastructure systems, there is a paucity of publications addressing the influence of algorithm-specific control parameters that impact the correct and efficient application of the selected identification scheme. Furthermore, as dense sensor arrays become widely accessible in civil infrastructure applications, voluminous amounts of multichannel data streams are becoming available for processing, thus imposing new demands on identification procedures regarding high-dimensionality ͑in both the spatial as well as the temporal domains͒ requirements that may render some methods inapplicable if careful attention is not paid to practical implementation issues. This paper provides a comprehensive study of three time-domain identification algorithms applied in conjunction with the Natural Excitation Technique in order to extract the modal parameters of a newly constructed long-span bridge that was monitored, in its virgin state, over a relatively long period of time with a state-of-the-art dense sensor array. The three methods used are: the eigensystem realization algorithm ͑ERA͒, the ERA with data correlations, and the least squares algorithm. One of the critical issues in the mentioned algorithms, is selection of the reference degree-of-freedom ͑DOF͒. Previous experiences have shown that one cannot rely on a single reference DOF for identification of all modes. Consequently, the aforementioned identification formulations were modified to include multiple reference DOF, simultaneously, or one at a time. An autonomous algorithm was presented to distinguish the genuine structural modes from spurious noise or computational modes. Based on some parameter studies, some useful guidelines for the selection of critical user-selectable parameters are presented.

No abstract