Monitoring of reinforced concrete beam damages using EMD-based acoustic emission and digital imaging correlation technologies

A comprehensive experimental method for monitoring and characterizing the damage evolution of Reinforced Concrete (RC) beams was conducted under three-point bending load, using a combination of Hilbert-Huang Transform (HHT) based Acoustic Emission (AE) technology and Digital Image Correlation (DIC). AE sensors capture the passive elastic waves emitted during damage progression, and their signals are analyzed using Empirical Mode Decomposition (EMD) to extract key features such as frequency and amplitude. Simultaneously, DIC and strain gauges monitor the deformation and stress distribution on the beam surface and reinforcement. The experimental results demonstrate a clear correlation between AE signal characteristics and the various stages of crack initiation, propagation, and structural failure. AE signals exhibit stage-specific spectral changes, evolving from low-frequency, low-amplitude patterns during micro crack formation to high-frequency, high-amplitude signals during major crack propagation. The DIC strain fields visually capture deformation concentration zones that align well with AE signal trends. This integrated monitoring approach enables the quantitative evaluation of structural damage and offers a reliable method for health monitoring of concrete structures. Subsequently, a database of characteristic parameters for different damage stages is established, providing theoretical support for the application of HHT in analyzing non-stationary signals in Structural Health Monitoring (SHM). • Combined with DIC data, the stress-strain behavior and damage progression of the RC beam under different loading conditions are captured. • Various characteristic vales such as frequency, amplitude, and load during the expansion and damage evolution process are obtained. • The results listed are the achievements of whole-field deformation measurement and damage monitoring throughout the entire process.