A Damage Detection Strategy Based on Autoregressive Parameters

Structural Health Monitoring (SHM) based on Operational Modal Analysis (OMA) is pivotal in assessing the integrity of structures and infrastructures in dynamic regimes. However, the successful extraction of modal parameters and damage indexes through OMA typically relies on a dense network of sensors working synchronously. This research aims at alleviating this issue by resorting to autoregressive (AR) models computed at individual sensing locations for damage detection, paving the way to a fully decentralized monitoring approach. Such framework, in which sensors can extract AR parameters in an independent manner, is explored to alleviate the need for strict data synchronization, which is instead a typical requirement of OMA procedures. The Mahalanobis distance is then used in combination with the Receiver Operating Curve (ROC) as a damage indicator to identify potential anomalies upon aggregating the collected sets of AR features from different sensors. The methodology has been applied to a numerical model and a real steel bridge, comparing the performance of the proposed damage detection strategy with a traditional approach based on modal parameters. Results demonstrate that the proposed AR-based procedure can be very competitive over a pure natural frequency-driven alternative, reaching a classification score as high as 98% in both scenarios.