用贝叶斯神经网络快速评估老化桥梁风险，精准识别需干预的结构。
Bayesian Surrogates for Risk-Aware Pre-Assessment of Aging Bridge Portfolios
- 基于贝叶斯神经网络构建桥梁结构评估代理模型，支持大规模快速分析。
- 在真实铁路下穿隧道案例中，准确预测结构合规性因子并量化不确定性。
- 适合基础设施管理者用于低成本、高可信度的风险预筛查，降低碳排放。
老化基础设施组合带来关键资源分配挑战：如何决定哪些结构需干预，哪些可安全服役。结构评估需权衡低成本保守方法与高精度但昂贵且难以扩展的仿真之间的矛盾。本文提出贝叶斯神经网络（BNN）代理模型，用于全球常见桥型（如钢筋混凝土框架桥）的快速结构预评估。模型基于瑞士联邦铁路桥梁组合开发的参数化管道生成的大规模非线性有限元分析数据库进行训练，能高效准确地预测高保真结构分析结果，输出符合规范的因子并校准认知不确定性。该BNN代理模型实现快速、带不确定性的风险筛查，可标记潜在高风险结构，并指引需进一步精细分析的区域。在真实铁路下穿隧道案例中验证了其有效性，证明该框架可显著减少不必要的分析与实体干预，从而大幅降低整体成本与碳排放。
原文摘要 · Abstract (English)
Aging infrastructure portfolios pose a critical resource allocation challenge: deciding which structures require intervention and which can safely remain in service. Structural assessments must balance the trade-off between cheaper, conservative analysis methods and accurate but costly simulations that do not scale portfolio-wide. We propose Bayesian neural network (BNN) surrogates for rapid structural pre-assessment of worldwide common bridge types, such as reinforced concrete frame bridges. Trained on a large-scale database of non-linear finite element analyses generated via a parametric pipeline and developed based on the Swiss Federal Railway's bridge portfolio, the models accurately and efficiently estimate high-fidelity structural analysis results by predicting code compliance factors with calibrated epistemic uncertainty. Our BNN surrogate enables fast, uncertainty-aware triage: flagging likely critical structures and providing guidance where refined analysis is pertinent. We demonstrate the framework's effectiveness in a real-world case study of a railway underpass, showing its potential to significantly reduce costs and emissions by avoiding unnecessary analyses and physical interventions across entire infrastructure portfolios.
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