Abstract
This paper aims at analyzing the feasibility of applying a vibration based damage detection approach, based on Principal Components Analysis (PCA), to eliminate environmental effects using the large amount of high quality data continuously collected by the dynamic monitoring system of Pedro e Inês footbridge since 2007. Few works describe real data, regularly collected along several years by reliable continuous dynamic monitoring systems in bridge structures. One main contribution is to show a large difference between making academic research based on numerical simulations or limited experimental samples, and making validity tests of innovative procedures using large high quality databases collected in real structures. The monitoring system, installed with the only initial objective of checking the efficiency of vibration control devices used to mitigate lateral and vertical vibrations, was therefore further developed for research purposes by implementing LabVIEW based automated signal processing and output-only modal identification routines, that enabled the analysis of the correlation of modal estimates with the temperature and the vibration level, as well as the automatic tracking of modal parameters along several years. With the final purpose of detecting potential structural damage at an early stage, the Principal Components Analysis (PCA) was employed to effectively eliminate temperature effects, whereas Novelty Analysis on the residual errors of the PCA model was used to provide a statistical indication of damage. The efficiency of this vibration based damage detection approach was verified using 3 years of measurements at Pedro e Inês footbridge under operational conditions and simulating several realistic damage scenarios affecting the boundary conditions. It is demonstrated that such a dynamic monitoring system, apart from providing relevant instantaneous dynamic information, working as an alert system associated to the verification of vibration serviceability limits, can also serve as an effective tool for long term bridge health monitoring.
Published Version
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