The overarching goal of this research is to derive innovative methodologies for analyzing diverse sensing streams to yield actionable information that directly support post-earthquake response, emergency management, and disaster recovery. As a step towards this goal, the objective of this study is to use data collected from the California Strong Motion Instrumentation Program (CSMIP) to demonstrate that a particular class of system identification (SI) techniques – namely, subspace identification (SI) or recursive subspace identification (RSI) – is especially suitable for rapid, post-disaster, structural health assessment. The advantage of SI/RSI is that it is an input-output and data-driven method, where only structural response records (e.g., acceleration records) are needed for extracting dynamic properties of the structure. In this paper, both SI and RSI were be applied to assess CSMIP-instrumented buildings with acceleration records from past ground motion events. The result verified that building dynamic characteristics (i.e., natural frequencies and mode shapes) could be clearly identified using all the recorded data simultaneously. In addition, the RSI algorithm was also employed for analyzing data recorded from the Northridge earthquake event. Time-varying modal properties of the building were also examined.
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