基于函数数据的结构健康监测：两个案例研究
Structural Health Monitoring with Functional Data: Two Case Studies
AI总结：
本文针对结构健康监测中的海量传感器数据流，提出结合函数对函数回归与轮廓监测的方法，并在德国和比利时桥梁案例中验证其能有效调整环境诱导变化并检测结构变化。
摘要：
结构健康监测（SHM）在土木工程中的应用日益广泛。其主要目的之一是检测并评估基础设施状况的变化，以减少可能的维护停机时间并提高安全性。理想情况下，该过程应自动化并实时实施。传感器技术的最新进展促进了数据收集和过程自动化，从而产生了海量数据流。函数数据分析（FDA）可用于以透明且可解释的方式对获取的数据进行建模和汇总。在德国和比利时桥梁的两个真实世界案例研究中，本文展示了如何将函数对函数回归方法与轮廓监测相结合，应用于SHM数据，以调整传感器/系统输出中的环境诱导变化，并检测建筑结构中的变化。具体而言，我们考虑了R包\ exttt{funcharts}，并讨论了在真实世界SHM数据上使用该软件时的一些挑战。例如，我们展示了数据的预平滑可以改善并扩展其可用性。
英文摘要：
Structural Health Monitoring (SHM) is increasingly used in civil engineering. One of its main purposes is to detect and assess changes in infrastructure conditions to reduce possible maintenance downtime and increase safety. Ideally, this process should be automated and implemented in real-time. Recent advances in sensor technology facilitate data collection and process automation, resulting in massive data streams. Functional data analysis (FDA) can be used to model and aggregate the data obtained transparently and interpretably. In two real-world case studies of bridges in Germany and Belgium, this paper demonstrates how a function-on-function regression approach, combined with profile monitoring, can be applied to SHM data to adjust sensor/system outputs for environmental-induced variation and detect changes in construction. Specifically, we consider the R package \texttt{funcharts} and discuss some challenges when using this software on real-world SHM data. For instance, we show that pre-smoothing of the data can improve and extend its usability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
