The field of Structural Health Monitoring (SHM) has received considerable attention for its
potential applications to monitoring civil infrastructure. However, the damage detection
algorithms that form the backbone of these systems have primarily been tested on simulated data
instead of full-scale structures because of the scarcity of real structural acceleration data. In
response to this deficiency in testing, we present the performance of two damage detection
algorithms used with ambient acceleration data collected during the staged demolition of the fullscale
Z24 Bridge in Switzerland. The algorithms use autoregressive coefficients as features of
the acceleration data and hypothesis testing and Gaussian Mixture Modeling to detect and
quantify damage. While experimental or numerically simulated data have provided consistently
positive results, field data from real structures, the Z24 Bridge, show that there can be significant
false positives in the predictions. Difficulties with data collection in the field are also revealed
pointing to the need for careful signal conditioning prior to algorithm application.
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