The monitoring of infrastructure assets using sensor networks is becoming increasingly prevalent. A digital twin in the form of a finite element model, as used in design and construction, can help in making sense of the copious amount of collected sensor data. This study demonstrates the application of the statistical finite element method (statFEM), which provides a consistent and principled means for synthesising data and physics-based models, in developing a digital twin of an instrumented railway bridge. The considered structure is a steel skewed half-through bridge of 27.34 m length located along the West Coast Mainline near Staffordshire in the UK. Using strain data captured from fibre Bragg grating (FBG) sensors at 108 locations along the bridge superstructure, statFEM can predict the `true' system response while taking into account the uncertainties in sensor readings, applied loading and finite element model misspecification errors. Longitudinal strain distributions along the two main I-beams are both measured and modelled during the passage of a passenger train. The digital twin, because of its physics-based component, is able to generate reasonable strain distribution predictions at locations where no measurement data is available, including at several points along the main I-beams and on structural elements on which sensors are not even installed. The implications for long-term structural health monitoring and assessment include optimization of sensor placement, and performing more reliable what-if analyses at locations and under loading scenarios for which no measurement data is available.
翻译:使用传感器网络监测基础设施资产的工作正在日益普遍。设计和建造中使用的限定要素模型形式的数字双胞胎可以帮助人们理解所收集的传感器数据数量之多。本研究展示了统计有限要素方法(STAFEM)的应用情况,该方法为综合数据和物理模型提供了一种一致和原则性的方法,用于综合数据和物理模型模型,以开发仪器化铁路桥梁的数字化双胞胎。所考虑的结构是位于联合王国斯塔福德郡附近的西海岸干线沿线27.34米长的钢片断半通桥。在桥顶结构108个地点的纤维布拉格(FBG)测量传感器采集的紧张数据,StagFEM可以预测“真实”系统的反应,同时考虑到传感器阅读、应用装货和有限要素模型辨别错误的不确定性。两种主要I-波束沿线的纵向紧张分布在客运列通过时得到测量和模拟。由于基于物理学的成份,数字双胞得以在那些甚至没有进行结构优化分析的场所进行合理的紧张分布预测,这些地方甚至没有进行结构化分析,其中包括在几个地点进行结构优化的测量数据分析。