发表机构
Tsinghua University; Xiandai Investment Co., Ltd.（清华大学; 现代投资有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
MoRF-AST通过带上下文条件仿射扩散传输的模态残差流匹配，解决工况变化下结构监测的概率虚拟传感校准问题，在桥梁基准测试中提升了后验精度与跨域覆盖误差性能，为土木工程提供数据高效的概率重构框架。
AI 中文摘要
概率全场重构为结构可靠性评估提供了感知不确定性的响应证据，但从稀疏且含噪的测量值进行推断仍存在不确定性不足的问题。多数现有方法忽略离线训练分布与实际工况分布之间的偏移，在这种偏移下，后验区间可能出现校准错误，导致报告的不确定性失去概率意义。本研究提出了带上下文条件仿射扩散传输的模态残差流匹配（MoRF-AST），用于工况变化下的校准结构虚拟传感。MoRF在归一化模态坐标中构建解析高斯参考后验，且仅在后验白化残差上训练条件流；部署阶段，AST从已安装传感器的历史测量值中估计响应尺度，并使用门控、保均值的Bures-Wasserstein传输来调整后验扩散。在桥梁甲板基准测试中，MoRF的后验均值归一化均方根误差（NRMSE）为7.20%，而两种直接条件流的NRMSE分别为16.1%和17.9%；在8个偏移交通域中，AST将MoRF的跨域平均覆盖误差从0.0535降至0.0236，降幅达55.9%，同时保持后验均值的准确性。该传输未整体提升所测试的替代方法，表明校准增益要求其方向与基础后验的离散度偏差匹配。MoRF-AST为概率全场重构提供了数据高效的框架，其不确定性在尺度主导的工况分布偏移下仍可解释；更广泛而言，本研究强调了在工况分布变化下校准不确定性的必要性，从而支持土木与基础设施工程中可信的概率建模及可靠性导向的决策制定。
英文摘要
Probabilistic full-field reconstruction provides uncertainty-aware response evidence for structural reliability assessment, yet inference from sparse and noisy measurements remains underdetermined. Most existing methods overlook shifts between offline training and operational distributions. Under such shifts, posterior intervals may become miscalibrated, causing the reported uncertainty to lose its probabilistic meaning. This study proposes Modal Residual Flow Matching with Context-Conditioned Affine Spread Transport (MoRF-AST) for calibrated structural virtual sensing under changing operating conditions. MoRF constructs an analytic Gaussian reference posterior in normalized modal coordinates and trains a conditional flow only on posterior-whitened residuals. At deployment, AST estimates response scale from historical measurements at installed sensors and uses gated, mean-preserving Bures-Wasserstein transport to adjust posterior spread. On a bridge-deck benchmark, MoRF achieves a posterior-mean normalized root-mean-square error (NRMSE) of 7.20%, compared with 16.1% and 17.9% for two direct conditional flows. Across eight shifted traffic domains, AST reduces MoRF's cross-domain average coverage error from 0.0535 to 0.0236, a 55.9% reduction, while preserving posterior-mean accuracy. The same transport does not improve the tested alternatives in aggregate, showing that calibration gains require its direction to match the base posterior's dispersion bias. MoRF-AST provides a data-efficient framework for probabilistic full-field reconstruction whose uncertainty remains interpretable under scale-dominated operational distribution shifts. More broadly, this work highlights the need to calibrate uncertainty under changing operational distributions, thereby supporting trustworthy probabilistic modeling and reliability-informed decision-making in civil and infrastructure engineering.
