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Abstract Bayesian inference based on computational simulations plays a crucial role in model-informed damage diagnostics and the design of reliable engineering systems, such as the miter gates studied in this article. While Bayesian inference for damage diagnostics has shown success in some applications, the current method relies on monitoring data from solely the asset of interest and may be affected by imperfections in the computational simulation model. To address these limitations, this article introduces a novel approach called Bayesian inference-based damage diagnostics enhanced through domain translation (BiEDT). The proposed BiEDT framework incorporates historical damage inspection and monitoring data from similar yet different miter gates, aiming to provide alternative data-driven methods for damage diagnostics. The proposed framework first translates observations from different miter gates into a unified analysis domain using two domain translation techniques, namely, cycle-consistent generative adversarial network (CycleGAN) and domain-adversarial neural network (DANN). Following the domain translation, a conditional invertible neural network (cINN) is employed to estimate the damage state, with uncertainty quantified in a Bayesian manner. Additionally, a Bayesian model averaging and selection method is developed to integrate the posterior distributions from different methods and select the best model for decision-making. A practical miter gate structural system is employed to demonstrate the efficacy of the BiEDT framework. Results indicate that the alternative damage diagnostics approaches based on domain translation can effectively enhance the performance of Bayesian inference-based damage diagnostics using computational simulations. 

Miter gates are vital civil infrastructure components in inland waterway transportation networks. To provide risk-informed insights for decisions related to repair and maintenance, sensors have been installed on some miter gates for monitoring. Despite the monitoring system's ability in collecting a large volume of monitoring data, accurately diagnosing damage state in such large structures remains challenging due to the lack of labeled monitoring data, since these structures are designed with high reliability and for a long operation life. This paper addresses this challenge by proposing a damage diagnostics approach for miter gates based on domain adaptation. The proposed approach consists of two main modules. In the first module, Cycle-Consistent generative adversarial network (CycleGAN) is employed to map monitoring data of a miter gate of interest and other similar yet different miter gates into the same analysis domain. Subsequently, a normalizing flow-based likelihood-free inference model is constructed within this common domain using data from source miter gates whose damage states are labeled from historical inspections. The trained normalizing flow model is then used to predict the damage state of the target miter gate based on the translated monitoring data. A case study is presented to demonstrate the effectiveness of the proposed method. The results indicate that the proposed method in general can accurately estimate the damage state of the target miter gate in the presence of uncertainty.