ALBERT

Description: Recent events of significant seismic activity (L' Aquila 2009, Christchurch 2011), have highlighted the importance of accurately assessing the properties of structures, for estimating their risk in future earthquakes as well as detecting and quantifying damage following an event. This demand may be fulfilled through implementation of SHM methodologies, which offer a means of assessing structural condition through the use of sensor data. Furthermore, the emergence of various sensor technologies has improved the quality of collected signals and made the idea of fusing heterogeneous data more appealing. Similar steps were achieved in advancing system identification algorithms. Several methods such as the Autoregressive Moving Average, Least Squares Estimation, Eigensystem Realization Algorithm, and Subspace State-Space System Identification (SSID) perform well in identifying elastic properties. However, the resulting matrices are often not obtained with respect to the physical basis, which is often desirable. Such a representation is obtained by default in Bayesian estimation methods, as the Unscented Kalman Filter and Particle Filters, which have recently been applied in the literature for data fusion problems. In this work, an experimental structure mounted on a shake table is monitored using accelerometers and displacement sensors. A method is developed (T-SSID) to transform the results of the SSID onto the physical basis. The introduced method and the Unscented Kalman Filter are employed identifying the structural properties and detecting an artificially induced in the original system damage. The performance of various sensor setups, which include fusion of heterogeneous sensors, are investigated to illustrate the performance of both methods, their ability to localize damage, and the benefits of data fusion. © 2014 John Wiley & Sons, Ltd.