In this paper, the feasibility of structural health monitoring based on natural frequencies is investigated for a steel bowstring railway bridge in Leuven, Belgium. The data used in the study are obtained from an ongoing long-term monitoring campaign on the railway bridge and include acceleration measurements on the bridge deck and the arches. During the monitoring period, the railway bridge has been retrofitted, resulting in data for two distinct states of the structure. Particular attention is paid to removing the effects of environmental conditions, such as temperature, which affect the modal characteristics of the structure and therefore may lead to false-positive or false-negative damage detection. A comparison is made between standard linear regression and robust principal component analysis (PCA), two black-box modeling techniques which are adopted to remove natural frequency variations resulting from changes in the environmental conditions. In order to assess the success rate of these techniques, a receiver operating characteristic (ROC) curve analysis is performed, considering the actual retrofit as well as a number of more subtle structural changes, which are modeled using a detailed finite element model of the structure. The state transition can be observed for the actual retrofit as well as for smaller structural modifications that result in relatively small natural frequency shifts.
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