Uncertainty-Based Damage Identification Using Cloud Model and Model Updating Techniques

Uncertainty factors in damage identification infiltrate and interact with each other, significantly influencing the outcomes of structural damage identification. Therefore, a method for uncertainty-based damage identification using stochastic model updating and the KullbackLeibler divergence based cloud model (KLDCM) is proposed. First, the uncertainty of the measured data in different scenarios is quantified by using the numerical characteristic parameter of the cloud model. The measured data are extended using cloud generator. Second, the structural physical parameters corresponding to the expanded data are calculated based on the improved stochastic model updating process. Based on the outer envelope curve of the cloud model, the excursion degree of the physical parameters for each structure element under both unknown scenarios and healthy scenarios is calculated. The mean value of the normalized excursion degree index for each element is then used as the threshold to identify the location of the damage element. Once the damage location is identified, the extent of damage is assessed by the expected values of the physical parameters of the damaged element. The feasibility and reliability of the proposed method are verified through numerical simulation and actual structure tests. Then, the effects of the noise level and number of original samples on the damage identification results are discussed. The results indicate that the proposed method is less susceptible to uncertain factors and achieves higher accuracy in damage identification compared to the method of maximum boundary curve method (MCM). © 2024 Nanjing University of Aeronautics an Astronautics. All rights reserved.