Enhancing seismic fragility and risk assessment of nuclear power plants relies on accurate prediction of reactor building responses to seismic hazards, which can be further improved through dynamic analysis of high-fidelity finite element (FE) models. However, FE models often exhibit non-negligible discrepancies from actual structures due to various sources of uncertainty, necessitating FE model updating with rigorous quantification of associated uncertainties. This paper presents a GPU-accelerated latent space--based Bayesian framework for FE model updating of building structures. In the proposed framework, high-dimensional structural response data (e.g., time histories or frequency response functions) are projected into a low-dimensional latent space using a multimodal variational autoencoder (MVAE), thereby enabling efficient and tractable likelihood evaluation without explicit modeling in the original observation space. Once trained, the surrogate enables amortized inference, allowing posterior sampling to be performed without additional simulator evaluations. We specifically employ a sequential Monte Carlo (SMC) sampler, whose population-based formulation allows parallel evaluation of the approximate likelihood on GPUs, resulting in computational efficiency and robustness against multimodal and complex posterior distributions. The proposed framework is validated through both numerical benchmarking and experimental data from a shaking table test of a reinforced concrete building structure. The results demonstrate that the method accurately estimates structural parameters with well-quantified uncertainties, while achieving fast and efficient inference through GPU-based parallelization, and enabling robust inference even in the presence of sparse observations that induce multimodal and highly complex posterior distributions.

翻译：提升核电站地震易损性与风险分析依赖于对反应堆建筑在地震灾害下响应的精准预测，而通过高保真有限元模型的动力分析可进一步改善该预测能力。然而，由于各种不确定性来源，有限元模型与实际结构常存在不可忽视的偏差，需在严格量化相关不确定性的前提下进行模型修正。本文提出了一种基于GPU加速的潜在空间贝叶斯框架，用于建筑结构的有限元模型修正。在该框架中，高维结构响应数据（如时程或频响函数）通过多模态变分自编码器投影至低维潜在空间，从而无需在原始观测空间显式建模即可实现高效且易处理的似然评估。训练完成后，该代理模型支持摊销推断，使其可在无需额外模拟器评估的情况下进行后验采样。我们特别采用了序贯蒙特卡洛采样器，其基于种群的计算方式允许在GPU上并行评估近似似然，从而对多模态及复杂后验分布兼具计算效率与鲁棒性。通过数值基准测试及一例钢筋混凝土建筑结构振动台试验数据验证了所提框架的有效性。结果表明，该方法能准确估计结构参数并充分量化其不确定性，同时通过基于GPU的并行化实现快速高效的推断，即便在观测数据稀疏（诱发多模态与高度复杂后验分布）的情况下仍能保持稳健的推断能力。