Infrastructure Anomaly Detection: A Study of the Settefonti Bridge Through Continuous OMA and Artificial Intelligence

This paper presents a novel approach for anomaly detection on the Settefonti Bridge by combining advanced operational modal analysis (OMA) technique with artificial intelligence. Ambient vibration data from accelerometers is processed using the covariance‐driven stochastic subspace identification (CoV‐SSI) method to extract modal parameters, which are then analyzed using statistical pattern recognition and Hotelling’s T2 control charts. In parallel, strain gauge time series data are evaluated with deep neural network‐based autoencoders that detect anomalies via reconstruction error analysis. This integrated framework enables robust, continuous structural monitoring and facilitates early damage detection, thereby supporting timely maintenance and enhanced safety of the bridge.