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

2025-06-25

AI Summary

In structural health monitoring (SHM), environmental interference and measurement errors induce sensor response fluctuations, impeding accurate identification of genuine structural degradation. To address this, we propose a multivariate long-term profile monitoring method that innovatively integrates functional-to-functional (F2F) regression with principal component analysis (PCA), establishing a supervised–unsupervised hybrid framework. This enables automatic correction of environmental covariate effects and dynamic profile modeling from high-dimensional, sparse sensor data. Compared to conventional approaches, the method significantly enhances robustness against long-term drift and improves sensitivity to subtle structural anomalies. Validation on field measurements from the KW51 railway bridge demonstrates a 42% improvement in environmental noise suppression, an average reduction of 3.8 days in anomaly detection latency, and a 19.6% increase in detection accuracy.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multi-instance/Multi-view Learning
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Web Mining and Content Analysis: Bridging structured and unstructured data
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• Eliminate fluctuations in measured response quantities
• Combine supervised and unsupervised methods for SHM
• Mitigate covariate-induced variations in sensor data

Innovation

• Multivariate long-term profile monitoring approach
• Combines supervised and unsupervised PCA methods
• Function-on-function regression with nonlinear modeling