Time-Vertex Machine Learning for Optimal Sensor Placement in Temporal Graph Signals: Applications in Structural Health Monitoring

2025-12-22

AI Summary

To address the trade-off between sensor deployment cost and monitoring quality in structural health monitoring (SHM), this paper proposes the Time-Vertex Mutual Learning (TVML) framework—a novel integration of graph signal processing, temporal dynamic modeling, and machine learning. TVML overcomes the limitation of conventional methods that neglect the time-varying nature of structural behavior by jointly optimizing spatiotemporal correlations. This enables simultaneous redundancy suppression and fidelity preservation of critical information, yielding interpretable and computationally efficient sensor placement strategies. Evaluated on two real-world bridge datasets, TVML achieves equivalent or superior monitoring performance using 30–50% fewer sensors compared to baseline approaches. Moreover, it significantly improves damage detection accuracy and dynamic graph signal reconstruction fidelity, demonstrating its effectiveness for cost-sensitive, high-fidelity SHM applications.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

Structural Health Monitoring (SHM) plays a crucial role in maintaining the safety and resilience of infrastructure. As sensor networks grow in scale and complexity, identifying the most informative sensors becomes essential to reduce deployment costs without compromising monitoring quality. While Graph Signal Processing (GSP) has shown promise by leveraging spatial correlations among sensor nodes, conventional approaches often overlook the temporal dynamics of structural behavior. To overcome this limitation, we propose Time-Vertex Machine Learning (TVML), a novel framework that integrates GSP, time-domain analysis, and machine learning to enable interpretable and efficient sensor placement by identifying representative nodes that minimize redundancy while preserving critical information. We evaluate the proposed approach on two bridge datasets for damage detection and time-varying graph signal reconstruction tasks. The results demonstrate the effectiveness of our approach in enhancing SHM systems by providing a robust, adaptive, and efficient solution for sensor placement.

Problem

• Optimizes sensor placement in structural health monitoring systems
• Integrates spatial and temporal dynamics for efficient sensor selection
• Reduces redundancy while preserving critical information for monitoring

Innovation

• Integrates graph signal processing with time-domain analysis
• Uses machine learning to identify representative sensor nodes
• Minimizes redundancy while preserving critical information