TY - GEN
T1 - A Portable Scanning Device for Local Vibration Testing of Concrete Structures
AU - Murakawa, Tatsuro
AU - Naito, Hideki
AU - Fujisaku, Yusuke
AU - Inaba, Kohko
AU - Takahashi, Takatada
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - In this study, the effectiveness of local vibration testing using a portable scanner and machine learning for non-destructive inspection of concrete structures was examined. Using a portable vibrator and laser vibrometer, local through-thickness vibration tests were conducted on specimens of concrete beams and a railway track containing voids. Comparisons between frequency response functions measured over intact and void regions within structural concrete showed wave damping around the void. Moreover, a large amount of frequency response functions was obtained with the laser vibrometer in the scanning testing. With the measured data, Support Vector Machine (Kernel method) was used for detecting voids within the beams and beneath the railway track slab. Through its analysis of frequency response functions, the measured data of the intact or void condition was classified with a percent accuracy of 70%. This result indicates promising usage of the proposed method: utilizing a portable vibrator, laser vibrometer, and machine learning for non-destructive inspection of concrete structures.
AB - In this study, the effectiveness of local vibration testing using a portable scanner and machine learning for non-destructive inspection of concrete structures was examined. Using a portable vibrator and laser vibrometer, local through-thickness vibration tests were conducted on specimens of concrete beams and a railway track containing voids. Comparisons between frequency response functions measured over intact and void regions within structural concrete showed wave damping around the void. Moreover, a large amount of frequency response functions was obtained with the laser vibrometer in the scanning testing. With the measured data, Support Vector Machine (Kernel method) was used for detecting voids within the beams and beneath the railway track slab. Through its analysis of frequency response functions, the measured data of the intact or void condition was classified with a percent accuracy of 70%. This result indicates promising usage of the proposed method: utilizing a portable vibrator, laser vibrometer, and machine learning for non-destructive inspection of concrete structures.
KW - Damage Detection
KW - Machine Learning
KW - Non-Destructive Testing
KW - RC Structures
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U2 - 10.1007/978-981-16-7216-3_7
DO - 10.1007/978-981-16-7216-3_7
M3 - Conference contribution
AN - SCOPUS:85121905230
SN - 9789811672156
T3 - Lecture Notes in Civil Engineering
SP - 71
EP - 82
BT - Proceedings of the 2nd International Conference on Structural Damage Modelling and Assessment - SDMA 2021
A2 - Abdel Wahab, Magd
PB - Springer Science and Business Media Deutschland GmbH
T2 - 2nd International Conference on Structural Damage Modelling and Assessment, SDMA 2021
Y2 - 4 August 2021 through 5 August 2021
ER -