Preprint Article Version 1 This version is not peer-reviewed

Computerized Data Interpretation for Concrete Assessment with Air-Coupled Impact-Echo: An Online Learning Approach

Version 1 : Received: 9 February 2018 / Approved: 9 February 2018 / Online: 9 February 2018 (06:55:24 CET)

A peer-reviewed article of this Preprint also exists.

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. 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.

Journal reference: Sensors 2018, 18, 833
DOI: 10.3390/s18030833

Abstract

Developing efficient Artificial Intelligence (AI)-enabled system to substitute human role in non-destructive testing is an emerging topic of considerable interest. In this study, we propose a novel impact-echo analysis system using online machine learning, which aims at achieving near-human performance for assessment of concrete structures. Current computerized impact-echo systems commonly employ lab-scale data to validate the models. In practice, however, the echo 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 echo characterization. More specifically, the proposed system can adaptively update itself to approaching human performance in impact-echo data interpretation. To this end, a two-stage framework has been introduced, including echo 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 echo instances from multiple inspection sites with each sample had been annotated by human experts with healthy/defective condition labels. The results demonstrated that the proposed scheme achieved favorable echo pattern classification accuracy with high efficiency and low computation load.

Subject Areas

non-destructive evaluation; hammering inspection; audio signal processing; machine learning; online learning

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