Model-based vs. Data-driven Approaches for Anomaly Detection in Structural Health Monitoring: a Case Study

Modern Structural Health Monitoring (SHM) systems are becoming of pervasive use in civil engineering because they can track the structural condition and detect damages of critical and civil infrastructures such as buildings, viaducts, and tunnels. Although noticeable work has been done to improve anomaly detection for ensuring public safety, algorithms that can be executed on low-cost hardware for long-term monitoring are still an open issue to the community. This paper presents a new framework that exploits compression techniques to identify anomalies in the structure, avoiding continuous streaming of raw data to the cloud. We used a real installation on a bridge in Italy to test the proposed anomaly detection algorithm. We trained three compression models, namely a Principal Component Analysis (PCA), a fully-connected autoencoder, and a convolutional autoencoder. Performance comparison is also provided through an ablation study that analyzes the impact of various parameters. Results demonstrate that the model-based approach, i.e., PCA, can reach a better accuracy whereas data-driven models, i.e., autoencoders, are limited by training set size.