Structural health monitoring (SHM) data is the essential foundation for any SHM structural integrity assessment, including large civil infrastructure such as the miter gate application in this work. For some applications, the amount of monitoring data is limited due to various reasons such as a lack of sensor deployment investment, sensor reliability, inaccessibility of measurement locations, expensive duty cycles, etc. This limited data could result in uncertainty in structural health assessment. This paper addresses this challenging issue by proposing a data augmentation method based on image translation for Bayesian inference-based damage diagnostics. In particular, we translate the monitoring data of one miter gate to that of another, thereby increasing the volume of monitoring data available for assessing the structural health of a target miter gate. This translation starts with converting the monitoring data of different miter gates into images. After that, Cycle Generative Adversarial Networks (CycleGAN) are employed to accomplish the task of image translation among different miter gates. The translated images (i.e., synthetic monitoring data) are then used together with the true monitoring data for damage diagnostics. Two types of CycleGAN architectures are investigated and compared using a case study. Results of the case study show that the proposed data augmentation method can effectively improve the accuracy and confidence of damage diagnostics of miter gates. It demonstrates the potential of integrating synthetic data generation with probabilistic model updating in structural health monitoring.

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