Acoustic Emission Time Series for Structural Failure Prediction: A Study on Corso Grosseto Viaduct Girders

Background: The ability to predict structural failures in civil engineering remains limited, particularly in assessing imminent failure conditions through real-time monitoring. This study addresses significant gaps in the identification of failure precursors in prestressed concrete structures. Objective: The primary objective of this research is to enhance the prediction of impending failures in bridge girders through advanced analysis of Acoustic Emission (AE) signals. Methods: Two innovative approaches are employed: Natural Time (NT) analysis and the Method of Critical Fluctuations-Based (MCF-B) approach. These methodologies are applied to AE data collected from girders subjected to three- and four-point bending tests, specifically those from a decommissioned 50-year-old prestressed concrete bridge in Turin, Italy. Results: The analysis reveals that critical regions identified through the convergence of Natural Time (NT) parameters correlate significantly with spikes in acoustic emission activity, indicating impending structural failure. The Method of Critical Fluctuations-Based (MCF-B) approach successfully identifies critical states, yielding consistent results across varying threshold levels, which reinforces its reliability. Conclusions: The findings underscore the effectiveness of both Natural Time (NT) and the Method of Critical Fluctuations-Based (MCF-B) approaches in predicting imminent structural failures, providing valuable insights for future structural health monitoring practices and enhancing the safety of aging infrastructure.