建筑结构在地震激励下的有限元模型更新：一种基于潜在空间的并行化贝叶斯框架
Finite element model updating of building structures under seismic excitation: A parallelized latent space-based Bayesian framework
AI总结：
本文提出一种基于潜在空间的并行化贝叶斯框架，用于建筑结构在地震激励下的有限元模型更新，通过GPU加速实现高效不确定性量化和参数估计。
摘要：
增强核电厂地震破坏和风险评估的可靠性依赖于准确预测反应堆建筑对地震危害的响应，这可以通过高保真有限元（FE）模型的动态分析进一步提高。然而，由于各种不确定性来源，FE模型往往与实际结构存在不可忽略的差异，需要通过严格量化相关不确定性的FE模型更新。本文提出了一种GPU加速的基于潜在空间的贝叶斯框架，用于建筑结构的FE模型更新。在所提出的框架中，高维结构响应数据（例如时间历史或频率响应函数）通过多模态变分自动编码器（MVAE）投影到低维潜在空间，从而在原始观测空间中无需显式建模即可实现高效且可操作的似然评估。一旦训练完成，该代理模型能够实现 amortized 推断，允许在不进行额外模拟器评估的情况下进行后验抽样。我们特别采用序列蒙特卡洛（SMC）采样器，其基于种群的公式允许在GPU上并行评估近似似然，从而实现计算效率并提高对多模态和复杂后验分布的鲁棒性。所提出的框架通过数值基准测试和钢筋混凝土建筑结构振动台试验数据进行了验证。结果表明，该方法能够准确估计结构参数并量化不确定性，同时通过GPU并行化实现快速高效的推断，并在稀疏观测导致多模态和高度复杂后验分布的情况下实现鲁棒推断。
英文摘要：
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.
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