Enhancing Bayesian Inference-Based Damage Diagnostics Through Domain Translation With Application to Miter Gates

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.