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Title
Japanese:計測波形分析への機械学習適用による高力ボルト軸力の超音波評価 
English:Ultrasonic High-Strength Bolt Axial Force Evaluation by Machine Learning-Based to Waveform Analysis 
Author
Japanese: 平尾賢生, 鈴木 啓悟, 森田 勝実, 伊藤裕一, 竹谷晃一, 佐々木栄一.  
English: Kensho Hirao, Keigo Suzuki, Katsumi Morita, Yuichi Ito, Kouichi Takeya, Eiichi Sasaki.  
Language Japanese 
Journal/Book name
Japanese:土木学会論文集A1（構造・地震工学） 
English:Journal of Japan Society of Civil Engineers, Ser. A1 (Structural Engineering & Earthquake Engineering (SE/EE)) 
Volume, Number, Page Vol. 78    No. 1    pp. 108-120
Published date Feb. 2022 
Publisher
Japanese:公益社団法人 土木学会 
English:Japan Society of Civil Engineers 
Conference name
Japanese: 
English: 
Conference site
Japanese: 
English: 
File
Official URL https://www.jstage.jst.go.jp/article/jscejseee/78/1/78_108/_article/-char/ja/
 
DOI https://doi.org/10.2208/jscejseee.78.1_108
Abstract Bolt axial force of high-strength bolted joints decreases due to deterioration. One of the conventional methods to evaluate the bolt axial force is ultrasonic testing. However, quantitative evaluation of the bolt axial force is controversial in terms of accuracy, and some problems remain for practical use. This study attempts the accurate evaluation of the bolt axial force by ultrasonic testing. The time zone analy-sis of the ultrasonic waves indicated that the waveform in the initial time zone includes bolt axial force information. The parasitic discrete wavelet transform (P-DWT) was applied to improve the evaluation accuracy. As a result of targeting bolts manufactured in the same lot, machine learning using linear re-gression evaluated the unknown bolt axial force within an error of 6% or less. Therefore, it was shown that the proposed method of this study corresponds to the existing bolt and can evaluate the bolt axial force with high accuracy.

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