Bayesian Damage Characterization Based on Perturbation Model Utilizing Responses at Vibration Nodes

Abstract

Most of the Structural Health Monitoring (SHM) methods are struggling among the number of sensors, the accuracy in damage detection and the requirement of a model. This paper explores the possibility of using a Bayesian probabilistic approach for damage imaging utilizing dynamic response at a few vibration nodes. In addition, the computational model in the Bayesian framework is replaced with a surrogate model based on perturbative solutions. This paper tries to propose a vibration-based SHM method which is suitable for real-time monitoring, requires a small number of industrial sensors, not rely on a high-fidelity FE model and can be applied for damage assessment of location and severity. In the present paper, several case studies demonstrate the advantage and feasibility of the proposed method.