| Project/Area Number |
18K04015
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 20010:Mechanics and mechatronics-related
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| Research Institution | University of Fukui |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
吉田 達哉 福井大学, 学術研究院工学系部門, 准教授 (20734544)
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| Project Period (FY) |
2018-04-01 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2020: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2019: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2018: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
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| Keywords | 打音検査 / コンクリート / 回転式打撃法 / 機械学習 / 周波数スペクトル / オーバーオール / 放射音 / 打撃音 / 押付力 / 振動モード / モデル同定 / 動的解析 |
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| Outline of Final Research Achievements |
Hammering sound test is most commonly used for defect inspection of concrete structures. In this study, we use a rotary hammering method and investigate hammering conditions for making the hammering sounds apparent to easily detect the defects. The results show that appropriate values are essential for the spring constant of a spring for pressing the rotary part and the pressing force. We propose a method for automatically detecting defects based on machine learning. The frequency spectra of hammering sounds at the hammering locations are used as input data and they are partitioned into some groups. The group with the smallest average overall value of frequency spectra is removed, resulting in identification of the defective parts.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究で用いた回転式打撃法は，打音検査を効率化し，かつ打撃力のばらつきを低減できる．研究成果では，回転打撃部の押付ばねのばね定数と押付力を適正に設定することで欠陥部での打撃音を顕在化できることを明らかにした．また，機械学習を用いて各打撃位置での打音データをグループ分けし，その後，欠陥判別指標を用いて健全部に相当するグループを取り除くことで欠陥部を自動的に検出することが可能となった．これらは，コンクリート構造物の打音検査の高度化に大きく寄与する．
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