Structural Health Monitoring with Functional Data: Two Case Studies

TL;DR

This paper demonstrates how functional data analysis, specifically function-on-function regression and profile monitoring, can enhance structural health monitoring by detecting changes and adjusting for environmental variations in real-world bridge data.

Contribution

It introduces a novel application of FDA techniques, including the use of the funcharts R package, to real-world SHM data for automated change detection and environmental adjustment.

Findings

Pre-smoothing improves data usability.

Function-on-function regression effectively detects structural changes.

Profile monitoring aids in environmental variation adjustment.

Abstract

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.