Artificial intelligence enhanced automatic identification for concrete cracks using acoustic impact hammer testing

Impact hammer testing is a regular structure inspection method for detecting surface and internal damages. Inspectors use the sound from impact hammer testing to determine the damaged area. However, manual impact hammer testing cannot meet the reliable accuracy for small damages, such as concrete cracks, and due to the shortage of experienced workers, a reliable tool is needed to evaluate the hammering sound. Therefore, to improve the detection accuracy, this study proposes an automatic crack identification process of impact hammer testing. Three approaches are used to identify crack characteristics, such as width, depth, and location, based on fast Fourier transformation for the hammering sound. To determine the relationship between damaged and intact information values, the first and second approaches use dominant frequency (D-f) and frequency feature value (V-f), respectively, whereas the last one uses Mel-frequency cepstral coefficients (MFCCs). Six concrete specimens with different crack widths and depths were fabricated to validate the three approaches. The experimental results reveal that although D-f can to detect the damage, it cannot classify its depth and width. Furthermore, V-f indicates the cracks, which are 20-mm deep. Three different artificial-intelligence classification algorithms were used to validate the MFCC approach, fuzzy rule, gradient boosted trees, and support vector machine (SVM). The three algorithms are applied and evaluated to enhance the acoustic impact hammer testing. The results reveal that the SVM algorithm confirms the ability and effectiveness for accurately identifying the concrete fine cracks that are 0.2-mm wide and 40-mm deep.