Transfer-learning guided Bayesian model updating for damage identification considering modeling uncertainty. (1st March 2022)

• Record Type: Journal Article
• Authors: Zhang, Zhiming; Sun, Chao; Guo, Beibei
• Is Part Of: Mechanical systems and signal processing. Volume 166(2022)
• Publication Date: 2022-03-01
• Subjects: Structural identification -- Model updating -- Hierarchical Bayesian inference -- Modeling uncertainty -- Domain adaptation -- Transfer learning; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2021.108426
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: Modeling uncertainty or modeling error has been widely recognized as one major challenge in structural model updating for structural identification and damage detection. It renders model updating inherently ineffective in converging to the real structural model because of the physical bias present in establishing the numerical model. This study aims to minimize the influence of modeling uncertainty during model updating so that the updated model can accurately indicate the damage state. To this end, this study proposes a methodology that applies pattern recognition methods to guide Bayesian model updating (BMU) and supervise the identification of structural damage. In detail, the transfer learning (TL) technique realized by domain adaptation is used to bridge the gap between the biased numerical model and the real structure and to guide the model updating process. Numerical and experimental studies have been implemented to validate the efficiency of domain adaptation in identifying the correct damage locations and the advantage of TL-guided BMU over the traditional method in identifying damage severities when modeling error exists. Moreover, this study proposes applying domain adaptation to bridge the gap between model-based and data-driven structural health monitoring (SHM) that are realized via model updating and pattern recognition, respectively. The proposed methodology is valuable and instructive for future work in this area. Highlights: The modeling uncertainty/error in model-based SHM is mitigated via transfer learning. Transfer learning bridges the gap between numerical models and real structures. Transfer learning identifies the correct damage locations via pattern recognition. Bayesian model updating accuracy is improved when large modeling error exists. This study integrates model-based and data-driven SHM synergistically.