A field experiment was conducted on a real continuous steel Gerber-truss bridge with artificial damage applied. This paper summarizes the results of the experiment for bridge damage detection utilizing traffic-induced vibrations. It investigates the sensitivities of a number of quantities to bridge damage including: the identified modal parameters and their statistical patterns, Nair's damage indicator (NDI) and its statistical pattern, and different sets of measurement points. The modal parameters are identified by autoregressive (AR) time-series models. The decision on bridge health condition is made and the sensitivity of variables is evaluated with the aid of the Mahalanobis-Taguchi system (MTS), a multivariate pattern-recognition tool. Several observations are made as follows. For the modal parameters, although bridge damage detection can be achieved by performing MTS on certain modal parameters of certain sets of measurement points, difficulties were faced in subjective selection of meaningful bridge modes and low sensitivity of the statistical pattern of modal parameters to damage. For NDI, bridge damage detection could be achieved by performing MTS on NDIs of most sets of measurement points. As a damage indicator, NDI was superior to modal parameters. Three main advantages were observed; it doesn't require any subjective decision in calculating NDI thus potential human errors can be prevented and an automatic detection task can be achieved, its statistical pattern has high sensitivity to damage, and finally, it is flexible regarding the choice of sets of measurement points.

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