Artificial intelligence enhanced automatic identification for concrete cracks using acoustic impact hammer testing
Artificial intelligence enhanced automatic identification for concrete cracks using acoustic impact hammer testing
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DOI：
10.1007/s13349-022-00651-8
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发表时间：
2022-11
影响因子：
4.4
通讯作者：
Mohamad Najib Alhebrawi;Huang Huang-Huang;Zhishen Wu
中科院分区：
文献类型：
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作者：
Mohamad Najib Alhebrawi;Huang Huang-Huang;Zhishen Wu
冲击锤检测是一种常规的结构检测方法，用于检测表面和内部损伤。检查员使用冲击锤测试的声音来确定损坏的区域。然而，对于混凝土裂缝等小损伤，人工冲击锤测试无法满足可靠的精度，并且由于缺乏经验丰富的工人，需要可靠的工具来评估锤击声。因此，为了提高检测精度，本研究提出了一种冲击锤检测裂纹的自动识别过程。基于锤击声的快速傅里叶变换，采用三种方法识别裂纹特征，如宽度、深度和位置。为了确定受损和完好信息值之间的关系，第一种和第二种方法分别使用主频率（）和频率特征值（），而最后一种方法使用Mel频率倒谱系数（MFCC）。制作了六个不同裂缝宽度和深度的混凝土试件来验证这三种方法。实验结果表明，该方法虽然能检测出损伤，但不能区分损伤的深度和宽度.此外，还显示了20 mm深的裂缝。三种不同的人工智能分类算法被用来验证MFCC方法，模糊规则，梯度提升树，和支持向量机（SVM）。这三种算法被应用和评估，以提高声冲击锤测试。结果表明，支持向量机算法证实了准确识别的能力和有效性，为0.2毫米宽，40毫米深的混凝土细裂缝。
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 () and frequency feature value (), 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 althoughcan to detect the damage, it cannot classify its depth and width. Furthermore,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.