Covariate-Adjusted Functional Data Analysis for Structural Health Monitoring

TL;DR

This paper introduces a covariate-adjusted functional data analysis framework for structural health monitoring, enabling nonlinear modeling of sensor data over long periods to improve change detection and safety assessment.

Contribution

It develops a flexible semi-parametric function-on-function regression method combining functional principal component analysis for effective long-term structural health monitoring.

Findings

Effective detection of structural changes over time

Handles large sensor data streams with covariate adjustments

Provides interpretable models for structural health assessment

Abstract

Structural Health Monitoring (SHM) is increasingly applied in civil engineering. One of its primary purposes is detecting and assessing changes in structure conditions to increase safety and reduce potential maintenance downtime. Recent advancements, especially in sensor technology, facilitate data measurements, collection, and process automation, leading to large data streams. We propose a function-on-function regression framework for (nonlinear) modeling the sensor data and adjusting for covariate-induced variation. Our approach is particularly suited for long-term monitoring when several months or years of training data are available. It combines highly flexible yet interpretable semi-parametric modeling with functional principal component analysis and uses the corresponding out-of-sample Phase-II scores for monitoring. The method proposed can also be described as a combination of an ``input-output'' and an ``output-only'' method.