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Sensors 2018, 18(3), 833; https://doi.org/10.3390/s18030833

Computerized Hammer Sounding Interpretation for Concrete Assessment with Online Machine Learning

1
National Metrology Institute of Japan (NMIJ), The National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba Central 2, Tsukuba 305-8568, Japan
2
Artificial Intelligence Research Center (AIRC), The National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki 305-8561, Japan
*
Author to whom correspondence should be addressed.
Received: 12 January 2018 / Revised: 3 March 2018 / Accepted: 5 March 2018 / Published: 9 March 2018
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Abstract

Developing efficient Artificial Intelligence (AI)-enabled systems to substitute the human role in non-destructive testing is an emerging topic of considerable interest. In this study, we propose a novel hammering response analysis system using online machine learning, which aims at achieving near-human performance in assessment of concrete structures. Current computerized hammer sounding systems commonly employ lab-scale data to validate the models. In practice, however, the response signal patterns can be far more complicated due to varying geometric shapes and materials of structures. To deal with a large variety of unseen data, we propose a sequential treatment for response characterization. More specifically, the proposed system can adaptively update itself to approach human performance in hammering sounding data interpretation. To this end, a two-stage framework has been introduced, including feature extraction and the model updating scheme. Various state-of-the-art online learning algorithms have been reviewed and evaluated for the task. To conduct experimental validation, we collected 10,940 response instances from multiple inspection sites; each sample was annotated by human experts with healthy/defective condition labels. The results demonstrated that the proposed scheme achieved favorable assessment accuracy with high efficiency and low computation load. View Full-Text
Keywords: non-destructive evaluation; hammer sounding; audio signal processing; machine learning; online learning non-destructive evaluation; hammer sounding; audio signal processing; machine learning; online learning
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Ye, J.; Kobayashi, T.; Iwata, M.; Tsuda, H.; Murakawa, M. Computerized Hammer Sounding Interpretation for Concrete Assessment with Online Machine Learning. Sensors 2018, 18, 833.

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