An Acoustic Emission Technique for Crack Modes Classification in Concrete Structures

• Main Authors: Viet Tra, Jae-Young Kim, Inkyu Jeong, Jong-Myon Kim
• Format: Article
• Language: English
• Published: MDPI AG 2020-08-01
• Series: Sustainability
• Subjects: reinforced concrete (RC) beams; acoustic emission; data-driven approaches; crack detection
• Online Access: https://www.mdpi.com/2071-1050/12/17/6724

The purpose of this study is to characterize fracture modes in a concrete structure using an acoustic emission (AE) technique and a data-driven approach. To clarify the damage fracture process, the specimens, which are of reinforced concrete (RC) beams, undergo four-point bending tests. During bending tests, impulses occurring in the AE signals are automatically detected using a constant false-alarm rate (CFAR) algorithm. For each detected impulse, its acoustic emission parameters such as counts, duration, amplitude, risetime, energy, RA, AF are calculated and studied. The mean and standard deviation values of each of these parameters are computed in every 1-second AE signal and are considered as features demonstrating the damage status of concrete structures. The results revealed that as the damage level in concrete structures grows, these features also change accordingly which can be used to categorize the damage fracture stages. The study also carries out experiments to validate the efficiency of the proposed approaches in terms of visual and qualitative evaluations. Experimental results show that the proposed characterizing model is promising and outstanding with the classification performance in the experimental environment of over 82%.