The study presents a comprehensive analysis of acoustic emission (AE) data collected from bending tests on prestressed reinforced concrete (RC) girders, with the aim of detecting and characterizing cracking for structural health monitoring (SHM) applications. Multiple assessment approaches are implemented, including both established and newly developed AE-based criteria, organized into distinct method specifications (MSs). For each MS, novel damage classification rules are proposed, and blind predictions are carried out to identify damage states ranging from microcrack initiation to macrocrack formation. The performance of each method is quantitatively evaluated using precision, recall, accuracy, and a global score enabling a comparative assessment. Results show that several entropy-based MSs achieve high predictive performance, and optimum assessment criteria are experimentally calibrated. The correlation between AE activity and damage progression is validated using additional specimens not involved in the blind phase. The study demonstrates the feasibility of using AE parameters for reliable damage classification in RC girders and provides a validated framework to support SHM procedures and future field applications.
D’Angela et al. (2025) studied this question.