Multivariate Long-term Profile Monitoring with Application to the KW51 Railway Bridge

TL;DR

This paper introduces a robust multivariate long-term profile monitoring method for Structural Health Monitoring that combines supervised and unsupervised techniques, effectively handling environmental fluctuations and measurement errors in large sensor datasets.

Contribution

It presents a novel function-on-function regression framework integrating nonlinear modeling to improve detection of structural changes in SHM.

Findings

Effective elimination of environmental fluctuations in sensor data

Enhanced detection accuracy of structural anomalies

Robustness in large and sparse sensor data streams

Abstract

Structural Health Monitoring (SHM) plays a pivotal role in modern civil engineering, providing critical insights into the health and integrity of infrastructure systems. This work presents a novel multivariate long-term profile monitoring approach to eliminate fluctuations in the measured response quantities, e.g., caused by environmental influences or measurement error. Our methodology addresses critical challenges in SHM and combines supervised methods with unsupervised, principal component analysis-based approaches in a single overarching framework, offering both flexibility and robustness in handling real-world large and/or sparse sensor data streams. We propose a function-on-function regression framework, which leverages functional data analysis for multivariate sensor data and integrates nonlinear modeling techniques, mitigating covariate-induced variations that can obscure structural changes.