Description
Strengthening and retrofitting systems, including FRCM (Fiber-Reinforced Cementitious Matrix), play a vital role in improving the durability and safety of structures facing challenges such as aging, increased loads, or seismic risks. To ensure the long-term integrity and performance of these structures, it's crucial to monitor the health of repaired structural components. Acoustic emission (AE) is a non-destructive and passive technique that involves the detection and analysis of stress waves emitted by various material components undergoing damage. In this particular research, the study focuses on the classification of damage in different material components of RC (Reinforced Concrete) beams strengthened with FRCM. This classification is achieved using a convolutional neural network (CNN) model that utilizes image-based waveform data from the AE testing. The study collected waveforms from four distinct failure modes: fabric-matrix debonding, fabric rupture, tensile cracking in the cementitious matrix, and yielding of steel. These waveforms were used to train the CNN model. Each waveform was converted into a Continuous Wavelet Transform (CWT) scalogram before being input into the model. The model demonstrated an overall prediction accuracy of approximately 92%. The pre-trained model was further employed to classify failure mechanisms in a full-scale RC beam that had been flexurally strengthened with FRCM. The overall test accuracy in this real-world field setting was found to be approximately 86%, which is considered a satisfactory level of accuracy.
