We suggest a multilevel model, to represent aggregate train-passing events from the Staffordshire bridge monitoring system. We formulate a combined model from simple units, representing strain envelopes (of each train passing) for two types of commuter train. The measurements are treated as a longitudinal dataset and represented with a (low-rank approximation) hierarchical Gaussian process. For each unit in the combined model, we encode domain expertise as boundary condition constraints and work towards a general representation of the strain response. Looking forward, this should allow for the simulation of train types that were previously unobserved in the training data. For example, trains with more passengers or freights with a heavier payload. The strain event simulations are valuable since they can inform further experiments (including FEM calibration, fatigue analysis, or design) to test the bridge in hypothesised scenarios.

翻译：我们提出了一种多层级模型，用于表示斯塔福德郡桥梁监测系统中聚合的列车通过事件。该模型由多个简单单元组合而成，这些单元代表两种通勤列车（每次通过时）的应变包络线。测量数据被视为纵向数据集，并通过（低秩近似）分层高斯过程进行表示。在组合模型的每个单元中，我们将领域专业知识编码为边界条件约束，并致力于构建应变响应的通用表示。展望未来，该方法应能模拟训练数据中未观测到的列车类型（例如，载客量更大的列车或重载货运列车）的通过事件。应变事件模拟具有重要价值，因为它可为后续实验（包括有限元模型标定、疲劳分析或设计）提供依据，以在假设场景下测试桥梁性能。