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Adaptive Excitation Frequency Matching and Acoustic-Feature-Based Automatic Hammering Inspection System

Huang Huang

Southeast University, International Institute for Urban Systems Engineering, Nanjing 211189, P. R. China

E-mail Address: hhly9527@gmail.com

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Xi Huang

Ibaraki University, Department of Urban and Civil Engineering, Hitachi 316-8511, Japan

E-mail Address: 22nd302r@vc.ibaraki.ac.jp

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Zhishen Wu

Southeast University, International Institute for Urban Systems Engineering, Nanjing 211189, P. R. China

E-mail Address: zswu@seu.edu.cn

Corresponding author.

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Mohamad Najib Alhebrawi

Ibaraki University, Department of Urban and Civil Engineering, Hitachi 316-8511, Japan

E-mail Address: najeebhub@gmail.com

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Zhishui Liang

Southeast University, International Institute for Urban Systems Engineering, Nanjing 211189, P. R. China

E-mail Address: zs_liang@seu.edu.cn

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https://doi.org/10.1142/S0219455423400382Cited by:1 (Source: Crossref)

This article is part of the issue:

Special Issue on Structural Engineering Dedicated to the 70th Birthday of Professor Y. B. Yang
Guest Editors: Weiyong Wang, C. W. Lim and Jie Yang

Abstract

Hammering tests are employed for detecting surface and internal damage; however, their accuracy depends on the experience and expertise of inspectors. Because automation techniques can address the limitations of manual hammering tests, researchers have developed automatic hammering inspection systems to replace or assist conventional hammering inspection. Although several studies on automatic hammering inspection have been reported, several challenges hinder the practical application of their results. The relative hammering frequency and identification algorithms for complex and diverse damage are critical factors in implementing developed techniques. In a previous study, an automatic hammering inspection device for rapid and automated damage detection in practical applications was developed. This device can remotely adjust the hammering angle and force to increase detection accuracy and efficiency. Furthermore, an artificial-intelligence-enhanced damage identification method was developed to identify fine concrete cracks accurately using acoustic data. This study developed an automatic hammering inspection system with an adaptive matching function and automatic damage identification to increase the accuracy and efficiency of damage detection. First, by imitating human auditory judgment, an adaptive matching algorithm for the excitation frequencies of different damage types was developed to determine the optimal excitation frequency. Second, the relationships between acoustic features and damage information were analyzed. Finally, two concrete specimens with artificial damage were tested using the proposed system, with the relationships between the acoustic features in the time and frequency domains, as well as the damage location and depth, investigated experimentally. The results show that the proposed system can accurately identify surface and internal damage distributions up to a depth of 100mm at 20mm intervals.

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