For existing bridges, reassessment of the structure and long-term performance analysis is a key issue. Recently, new techniques emerged, such as ‘drive-by’ bridge inspection for instance, making instrumentation much easier and inspections cheaper. Time-frequency analysis tools such as the continuous wavelet transform have been recognized for several years for their ability to process vibration response signals, and to identify, in the linear case, the modal parameters of a structure with a high precision. For existing bridges, without numerical or experimental reference solution, identification of local defects or non-linearities from SHM is then challenging. Without knowledge of threshold values not to be exceeded, for example, modal parameters or the axial deformation given by strain gauges or the vertical oscillations of the deck, an approach only oriented on signal processing can be quite limited.
The topic of this research concerns the proposition of indicators using all data collected from various sensors (accelerometers, strain gages, temperature sensors) during few months. Such indicators should be representative enough to allow an automatic structural health monitoring.
From data collection, we propose to identify an “eigen” dynamic signal, representative of dynamic signals obtained by accelerometers under passing vehicles, without any abnormal behaviors. Considering all the dynamic events (vehicle) impacting the structure over a long time (under road traffic) we identify criteria to detect a population of events that are dispersed in relation to the medium event characterized by the eigen “signal”. From such classification, we estimate which combination of data (acceleration, temperature, strain, etc.) are therefore more or less critical for the structure as some frequencies are only detect under specific loads (storm or overloaded vehicles for example). Proposition of the methodology is detailed with an application on a real bridge. This bridge was instrumented with strain gauges, accelerometers, and temperature sensors.