A novel approach for the structural identification and monitoring of a full-scale 17-story building based on ambient vibration measurements

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.