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

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Abstract

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.

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The raw/processed data required to reproduce these findings cannot be shared, where the data also forms part of ongoing research.

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Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: the authors gratefully acknowledge the financial support from the Priority Research Program of Ibaraki University.

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Authors and Affiliations

  1. Department of Urban and Civil Engineering, Ibaraki University, 4-12-1 Nakanarusawa, Hitachi, 316-8511, Japan

    Mohamad Najib Alhebrawi & Zhishen Wu

  2. Key Laboratory of Concrete and Prestressed Concrete Structure of the Ministry of Education, Southeast University, Nanjing, 210096, China

    Huang Huang

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  1. Mohamad Najib Alhebrawi
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Contributions

MNA: investigation, methodology, formal analysis, data curation, writing—original draft. Huang Huang: investigation, data curation. ZW: supervision, conceptualization, resources, data curation, project administration, writing—review and editing.

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Correspondence to Zhishen Wu.

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Alhebrawi, M.N., Huang, H. & Wu, Z. Artificial intelligence enhanced automatic identification for concrete cracks using acoustic impact hammer testing. J Civil Struct Health Monit 13, 469–484 (2023). https://doi.org/10.1007/s13349-022-00651-8

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