  The high structural deficient rate poses serious risks to the operation of
many bridges and buildings. To prevent critical damage and structural collapse,
a quick structural health diagnosis tool is needed during normal operation or
immediately after extreme events. In structural health monitoring (SHM), many
existing works will have limited performance in the quick damage identification
process because 1) the damage event needs to be identified with short delay and
2) the post-damage information is usually unavailable. To address these
drawbacks, we propose a new damage detection and localization approach based on
stochastic time series analysis. Specifically, the damage sensitive features
are extracted from vibration signals and follow different distributions before
and after a damage event. Hence, we use the optimal change point detection
theory to find damage occurrence time. As the existing change point detectors
require the post-damage feature distribution, which is unavailable in SHM, we
propose a maximum likelihood method to learn the distribution parameters from
the time-series data. The proposed damage detection using estimated parameters
also achieves the optimal performance. Also, we utilize the detection results
to find damage location without any further computation. Validation results
show highly accurate damage identification in American Society of Civil
Engineers benchmark structure and two shake table experiments.
