Long-term monitoring of a PSC box girder bridge: Operational modal analysis, data normalization and structural modification assessment

Abstract

For reliable performance of vibration-based damage detection algorithms, it is important to distinguish abnormal changes in modal parameters caused by structural damage from normal changes due to environmental fluctuations. This paper firstly addresses the modeling of temperature effects on modal frequencies of a PSC box girder bridge located on the A1 motorway in France. Based on a six-month monitoring experimental program, modal frequencies of the first seven mode shapes and temperatures have been measured at three hour intervals. Neural networks are then introduced to formulate regression models for quantifying the effect of temperature on modal parameters (frequencies and mode shapes). In 2009, this bridge underwent a strengthening procedure. In order to assess the effect of strengthening on the vibration characteristics of the bridge, modal properties had to be corrected from temperature influence. Thus, the first goal is to assess the changes on the vibration signature of this bridge induced by the strengthening. For this purpose, classical statistical analysis and clustering methods are applied to the data recorded over the period after strengthening. The second goal is to evaluate the influence of temperature effects on the clustering results. It comes that the temperature correction significantly improves the confidence in the novelty detection and in the strengthening efficiency.