This paper describes recent collaborative efforts made by the United States Geological Survey and Department of Veterans Affairs (VA) in real-time seismic monitoring of VA hospital buildings located in seismically active regions. The instrumentation in each building encompasses accelerometers deployed on all floors, a multi-channel recorder, and a server to analyze and archive the building’s dynamic response in real-time. The server runs advanced structural health monitoring software, which consists of several data processing and analysis modules. Four different algorithms are implemented in four separate modules to compute shear-wave travel time, modal parameters, base shear force, and inter-story drift ratio from the measured vibration data from the instrumented building. The performance level and damage state of the building are estimated from the inter-story drift ratio and base-shear; the change in modal parameters and wave travel time is also used to detect and locate any possible damage zone(s) in the building. These algorithms are validated and verified using data from full-scale shake table tests. The information obtained from the real-time seismic monitoring system can be used to support timely decisions regarding the structural integrity of the VA hospital buildings immediately after an earthquake, and to help with inspections and necessary repairs and replacements.
This paper deals with the realization of finite dimensional, linear, time-invariant models of structural systems in
the state space description from the response (output) of the system. The theory and and underlying principles
of two stochastic system identification algorithms are first described. The applications of the algorithms to
two civil engineering structures follow the theory. Ambient vibration data collected from a building and a
bridge, both are permanently instrumented by accelerometer networks, are used to derive the models. The
vibration characteristics, i.e., the frequencies, damping ratios, and associated mode shapes, of the structures are
then retrieved from the models. The stochastic system identification algorithms prove to be very effective in
identifying the vibration characteristics of the structures.
This paper describes the identification of finite dimensional, linear, time-invariant models of a 4-story building
in the state space representation using multiple data sets of earthquake response. The building, instrumented
with 31 accelerometers, is located on the University of California, Irvine campus. Multiple data sets, recorded
during the 2005 Yucaipa, 2005 San Clemente, 2008 Chino Hills, and 2009 Inglewood earthquakes, are used for
identification and validation. Considering the response of the building as the output and the ground motion as
the input, the state space models that represent the underlying dynamics of the building in the discrete-time
domain corresponding to each data set are identified. The four state space models identified demonstrate that
the response of the building is amplitude dependent with the response frequency, and damping, being dependent
on the magnitude of ground excitation. The practical application of this finding is that the consistency of this
building response to future earthquakes can be quickly assessed, within the range of ground excitations considered (0.005g - 0.074g), for consistency with prior response - this assessment of consistent response is discussed and demonstrated with reference to the four earthquake events considered in this study. Inclusion of data sets relating to future earthquakes will enable the findings to be extended to a wider range of ground excitation magnitudes.