Bayesian-Based Structural Damage Detection on the Integration of Global and Local Information

Abstract

Vibration-based damage detection methods have inherent uncertainties due to the perturbation of measurement noise, modeling errors, and environmental changes, such as variations in temperature. However, other measured local information, including the static strain and deflection measured by the structural health monitoring system, have not been taken full advantage of. The integration of such global and local information has the potential to lead to more accurate structural damage detection. This paper proposes a method for integrating the global and local information for structural damage detection based on Bayesian theory. First, the Bayesian probability model associated with natural frequencies, displacement mode shapes, and strain modes is developed. In this model, the local strain information is also used as an input. Second, to reduce the model's computational cost in complex structures, the strain energy damage index is employed to determine the potential damage ranges. Finally, the exact damage elements are detected by a proposed sequence elimination method. Numerical simulations on a 14-bay planar rigid structure and the experiment on a 20-bay rigid truss are carried out to demonstrate the effectiveness of the proposed method while considering model uncertainty and measurement noise. The results show that the proposed damage detection method can increase the model's identification accuracy and decrease its misjudgment rate, compared with the method that only uses global information.