Application of Structural Health Monitoring Techniques to Track Structural Changes in a Retrofitted Building Based on Ambient Vibration

Abstract

One of the issues complicating the reliability assessment of structural health monitoring (SHM) methodologies slated for implementation under field conditions for damage detection in conjunction with typical infrastructure systems, is the paucity of experimental measurements from such structures. Particularly lacking is the availability of experimental data from physical structures, where quantifiable changes are made in the structure while SHM studies are being performed. That is precisely the focus of this paper. As a result of the 1994 Northridge Earthquake, a critical six-story building in the metropolitan Los Angeles region was found to need significant seismic mitigation measures. The building was instrumented with 14 state-of-the-art strong-motion accelerometers that were placed at various locations and in different orientations throughout the building. The instrumentation network was used to acquire extensive ambient vibration data sets at regular intervals that covered the whole construction phase, during which the building evolved from its original condition to the retrofitted status. This paper evaluates the usefulness of the natural excitation technique (NExT) in conjunction with the eigensystem realization algorithm (ERA) to determine the evolution of the modal properties of the subject building during the various phases of its retrofit process. Further, an assessment is made of the influence on the system identification results of significant user-selectable parameters such as: data window size and overlap; reference degree-of-freedom; and the dimensions of the associated Hankel matrix. In spite of the very low levels of ambient excitation, and the low spatial resolution of the sensors, use of the NExT/ERA algorithm yielded excellent identification results of the dominant modes of the building. Changes in the identified structural frequencies are correlated with the time that specific structural changes were made. It is shown that this unique collection of data can be extremely useful in calibrating the accuracy and sensitivity of various SHM schemes, as well as in providing useful identification parameter guidelines that can assist in the planning and deployment of sensor networks and associated data collection schemes for SHM applications.