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

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