Structural Engineering and Mechanics

  • 제65권5호
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  • Pages.601-609
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  • 2018
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  • 1225-4568(pISSN)
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  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Evaluating the pull-out load capacity of steel bolt using Schmidt hammer and ultrasonic pulse velocity test

  • Saleem, Muhammad (Department of Mechanical and Energy Engineering, College of Engineering, Imam Abdulrahman Bin Faisal University)
  • 투고 : 2017.07.29
  • 심사 : 2018.01.03
  • 발행 : 2018.03.10
⟨ 이전 논문 다음 논문 ⟩

초록

Steel bolts are used in the construction industry for a large variety of applications that range from fixing permanent installations to temporary fixtures. In the past much research has been focused on developing destructive testing techniques to estimate their pull-out load carrying capacity with very little attention to develop non-destructive techniques. In this regards the presented research work details the combined use of ultrasonic pulse velocity and Schmidt hammer tests to identify anchor bolts with faculty installation and to estimate their pull-out strength by relating it to the Schmidt hammer rebound value. From experimentation, it was observed that the load capacity of bolt depends on its embedment length, diameter, bond quality/concrete strength and alignment. Ultrasonic pulse velocity test is used to judge the quality of bond of embedded anchor bolt by relating the increase in ultrasonic pulse transit time to the presence of internal pours and cracks in the vicinity of steel bolt and the surrounding concrete. This information combined with the Schmidt hammer rebound number, R, can be used to accurately identify defective bolts which resulted in lower pull-out strength. 12 mm diameter bolts with embedment length of 70 mm and 50 mm were investigated using constant strength concrete. Pull-out load capacity versus the Schmidt hammer rebound number for each embedment length is presented.

키워드

참고문헌

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피인용 문헌

  1. Assessing the load carrying capacity of concrete anchor bolts using non-destructive tests and artificial multilayer neural network vol.30, pp.None, 2020, https://doi.org/10.1016/j.jobe.2020.101260
  2. Concrete strength monitoring based on the variation of ultrasonic waveform acquired by piezoelectric aggregates vol.76, pp.5, 2018, https://doi.org/10.12989/sem.2020.76.5.591