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Recent studies in structural health monitoring have shown that damage detection algorithms based on statistical pattern recognition techniques for ambient vibrations can be used to successfully detect damage in simulated models. However, these algorithms have not been tested on full-scale civil structures, because such data are not readily available. A unique opportunity for examining the effectiveness of these algorithms was presented when data were systematically collected from a progressive damage field test on the Z24 bridge in Switzerland. This paper presents the analysis of these data using an autoregressive algorithm for damage detection, localization, and quantification. Although analyses of previously obtained experimental or numerically simulated data have provided consistently positive diagnosis results, field data from the Z24 bridge show that damage is consistently detected, however not well localized or quantified, with the current diagnostic methods. Difficulties with data collection in the field are also revealed, pointing to the need for careful signal conditioning prior to algorithm application. Furthermore, interpretation of the final results is made difficult due to the lack of detailed documentation on the testing procedure.

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