The Use of Vibration Data for Damage Detection in Bridges: A Comparison of System Identification and Pattern Recognition Approaches

Abstract

This paper briefly outlines the rationale for structural health monitoring as an integral component of bridge management systems. Two different approaches, system identification and statistical pattern recognition, are summarised and applied in turn to vibration data collected from three scale modelreinforced concrete bridges. The results show that the system identification paradigm can successfully locate and quantify the damage to the decks when they are loaded to incipient collapse, especially when experience is used to determine the parameters to use in the finite element updating procedure. However, the study also demonstrated that this approach requires a large amount of high quality data, requirements that cannot always be met readily in the field. In contrast, although the statistical pattern recognition approach was not able to quantify or locate the damage, it was able to clearly indicate that damage had occurred from relatively few measurements. A comparison of the strengths and weaknesses of the two approaches suggests that they should be used in a complementary manner. The statistical pattern recognition approach can be employed as a simple, cost efficient way to indicate that damage has occurred. It can then trigger a more detailed investigation using system identification.

Keywords

bridges finite elements structural health monitoring modal analysis artificial neural networks

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