A Computational Framework for Modeling Complex Sensor Network Data Using Graph Signal Processing and Graph Neural Networks in Structural Health Monitoring

TL;DR

This paper introduces a novel computational framework combining Complex Network Modeling, Graph Signal Processing, and Graph Neural Networks to analyze and predict sensor data in structural health monitoring, demonstrated on a large bridge case study.

Contribution

It presents an integrated framework that leverages GSP and GNNs for modeling complex sensor data in SHM, enhancing sensor importance identification and strain prediction capabilities.

Findings

GSP identifies key sensors and signal patterns effectively.

GNNs improve strain prediction accuracy.

Framework applied successfully to real-world bridge data.

Abstract

Complex networks lend themselves to the modeling of multidimensional data, such as relational and/or temporal data. In particular, when such complex data and their inherent relationships need to be formalized, complex network modeling and its resulting graph representations enable a wide range of powerful options. In this paper, we target this - connected to specific machine learning approaches on graphs for structural health monitoring on an analysis and predictive (maintenance) perspective. Specifically, we present a framework based on Complex Network Modeling, integrating Graph Signal Processing (GSP) and Graph Neural Network (GNN) approaches. We demonstrate this framework in our targeted application domain of Structural Health Monitoring (SHM). In particular, we focus on a prominent real-world structural health monitoring use case, i.e., modeling and analyzing sensor data (strain, vibration) of a large bridge in the Netherlands. In our experiments, we show that GSP enables the identification of the most important sensors, for which we investigate a set of search and optimization approaches. Furthermore, GSP enables the detection of specific graph signal patterns (mode shapes), capturing physical functional properties of the sensors in the applied complex network. In addition, we show the efficacy of applying GNNs for strain prediction on this kind of data.