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Open AccessArticle

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.
Sensors 2018, 18(3), 833; https://doi.org/10.3390/s18030833
Received: 12 January 2018 / Revised: 3 March 2018 / Accepted: 5 March 2018 / Published: 9 March 2018
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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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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