We present a statistical process control framework to support structural health monitoring
of transportation infrastructure. We contribute an integrated, generally-applicable (to various
types of structural response data) statistical approach that links the literatures on statistical
performance modeling and on structural health monitoring. The framework
consists of two parts: The first, estimation of statistical models to explain, predict, and control
for common-cause variation in the data, i.e., changes, including serial dependence, that
can be attributed to usual operating conditions. The ensuing standardized innovation series
are analyzed in the second part of the framework, which consists of using Shewhart and
Memory Control Charts to detect special-cause or unusual events