Interfacial debonding detection of steel beams reinforced by CFRP plates based on percussion method

Steel structure reinforced engineering makes extensive use of carbon fiber reinforced plastic (CFRP) plates. Although it happens frequently, the interfacial debonding of CFRP plate-reinforced steel beams has been the main cause of failure in flexural strengthened steel beams, which results in the brittle failure of steel structures. This paper proposes a novel percussion method to locate and quantify the debonding area based on the Welch power spectrum density (PSD) estimate, mel frequency cepstral coefficient (MFCC), and convolutional neural network (CNN). According to our knowledge, this is the first attempt to use the percussion method to identify and measure the debonding defects of steel beams strengthened by CFRP plates. Additionally, this experiment takes into account how percussion instruments affect localization and quantification. To begin with, the debonding location is qualitatively detected using the Welch PSD estimate method. The presented percussion method can successfully and accurately detect the interfacial debonding of CFRP plate-reinforced steel beams, according to repeated results. Subsequently, a novel CNN model is constructed to classify the various debonding areas based on the MFCC feature. In the end, the experimental findings show that the localization and quantification of the hammer types vary only slightly. Overall, the percussion-based approach can offer a fresh line of inquiry into the debonding problem on steel beams reinforced with CFRP plates.