桥梁数字孪生概率校准中嵌入模型形式不确定性
Embedding Model-form Uncertainty in Probabilistic Calibration of Digital Twins for Bridges
浏览论文内容
中文总结 AI 辅助
本研究提出一种通过随机参数嵌入显式量化模型形式不确定性的桥梁数字孪生校准框架，采用三步策略和方差分解，在Nibelungenbrücke桥梁上验证，提升了预测不确定性的可解释性与可靠性。
中文摘要 AI 辅助
桥梁的数字孪生依赖于基于物理的模型，从稀疏的监测数据中推断全场结构响应。这些预测的可靠性取决于模型参数的校准，同时需考虑模型与物理系统之间的差异。这些差异通常被称为模型形式不确定性（MFU），源于简化假设和对物理过程的不完整表示，并可能显著影响预测可靠性。因此，在校准过程中明确量化MFU对于可信的数字孪生预测至关重要。本研究提出了一种校准框架，通过随机参数嵌入明确表示MFU。该框架使用Nibelungenbrücke桥梁的简化二维截面热模型进行演示，该模型根据其监测系统的温度测量数据进行校准。通过明确考虑MFU，所提出的方法能够使计算效率高的模型在数字孪生环境中可靠使用，同时保持预测可信度。该框架引入了一种三步校准策略，逐步处理差异来源，同时保持已识别不确定性之间的明确区分。使用方差分解来表征预测不确定性的结构及其向感兴趣量的传播。预测分布与观测之间的剩余差异使用Kolmogorov-Smirnov指标进行量化，以评估跨季节条件下的预测一致性。结果表明，明确考虑MFU提高了预测不确定性的可解释性和可靠性，并验证了该方法用于支持结构健康监测中数字孪生的基于物理模型校准的有效性。
英文摘要
Digital twins of bridges rely on physics-based models to infer full-field structural responses from sparse monitoring data. The reliability of these predictions depends on the calibration of model parameters while accounting for discrepancies between the model and the physical system. Such discrepancies, commonly referred to as model-form uncertainty (MFU), arise from simplifying assumptions and incomplete representations of physical processes, and can substantially affect predictive reliability. Explicitly quantifying MFU during calibration is therefore essential for trustworthy digital-twin predictions. This work presents a calibration framework that explicitly represents MFU through stochastic parameter embedding. The framework is demonstrated using a simplified two-dimensional cross-sectional thermal model of the Nibelungenbrücke calibrated against temperature measurements from its monitoring system. By explicitly accounting for MFU, the proposed methodology enables computationally efficient models to be reliably employed within digital-twin environments while maintaining predictive credibility. The framework introduces a three-step calibration strategy that progressively addresses sources of discrepancy while maintaining a clear distinction between identified uncertainties. Variance decomposition is used to characterize the structure of predictive uncertainty and its propagation to quantities of interest. Remaining discrepancies between predictive distributions and observations are quantified using Kolmogorov-Smirnov metrics to assess predictive consistency across seasonal conditions. The results demonstrate that explicitly accounting for MFU improves the interpretability and reliability of predictive uncertainties and validate the methodology for calibration of physics-based models supporting digital twins in structural health monitoring.
发表机构
- Technical University of Munich（慕尼黑工业大学）
- Bundesanstalt für Materialforschung und -prüfung（德国联邦材料研究与测试研究所）
- Zuse Institute Berlin（柏林祖斯研究所）
机构由 AI 辅助整理，请以论文原文为准。
