Integration of Pattern Recognition and Machine Learning with the Acoustic Emission Method to Locate and Assess...

Prestressed and post-tensioned concrete structural elements constitute approximately 43.4% of modern bridge infrastructure, representing 58.2% of the total bridge surface area due to their long-span capabilities. Despite their structural efficiency, evaluating residual post-tensioning forces and diagnosing localized degradation within internally grouted tendons-such as localized stress corrosion cracking (SCC), grout voids, and moisture infiltration-remains a critical challenge due to geometric confinement and high material attenuation. This paper presents a non-destructive Structural Health Monitoring (SHM) methodology optimized for the continuous and periodic assessment of post-tensioned anchorage zones under operational traffic loads. The proposed Identification of Active Anomalies (IAA) system integrates the Acoustic Emission (AE) method with unsupervised machine learning to classify multi-mechanism structural degradation. By implementing a mathematically transparent k-means clustering framework initialized via the k-means++ heuristic, high-velocity multi-parameter AE data streams are partitioned within an n-dimensional Euclidean feature space. The scientific novelty of this work lies in its real-scale validation on an operational, highly complex cable-stayed bridge, establishing a previously unpublished acoustic signature database (the 2025 Signal Database). The empirical validity of the algorithm's predictive boundaries was confirmed through forensic physical inspections and material sampling during a major structural rehabilitation in 2026, which corroborated the active corrosion states within heavily confined post-tensioned anchorage blocks. Furthermore, extracted AE pattern classes are explicitly correlated with structural crack opening widths, enabling real-time tracking of macro-defect propagation, anchorage slippage, and active micro-structural corrosion.