Earthquakes and Structures

  • 제27권4호
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  • Pages.263-283
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  • 2024
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  • 2092-7614(pISSN)
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  • 2092-7622(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

Crack identification in beam-like structures using multi-mass system and wavelet transform

  • Siamak Ghadimi (Department of Civil Engineering, Faculty of Engineering, Urmia University) ;
  • Seyed Sina Kourehli (Department of Civil Engineering, Azarbaijan Shahid Madani University) ;
  • Gholamreza Zamani-Ahari (Department of Civil Engineering, Faculty of Engineering, Urmia University)
  • 투고 : 2024.05.14
  • 심사 : 2024.07.19
  • 발행 : 2024.10.25
⟨ 이전 논문 다음 논문 ⟩

초록

This research introduces a new composite system that utilizes multiple moving masses to identify cracks in structures resembling beams. The process starts by recording displacement time data from a set of these moving masses and converting this information into a relative time history through weighted aggregation. This relative time history then undergoes wavelet transform analysis to precisely locate cracks. Following wavelet examinations, specific points along the beam are determined as potential crack sites. These points, along with locations on the beam susceptible to cracked point due to support conditions, are marked as crack locations within the optimization algorithm's search domain. The model uses equations of motion based on the finite element method for the moving masses on the beam and employs the Runge-Kutta numerical solution within the state space. The proposed system consists of three successive moving masses positioned at even intervals along the beam. To assess its effectiveness, the method is tested on two examples: a simply supported beam and a continuous beam, each having three scenarios to simulate the presence of one or multiple cracks. Additionally, another example investigates the influence of mass speed, spacing between masses, and noise effect. The outcomes showcase the method's effectiveness and efficiency in localizing crack, even in the presence of noise effect in 1%, 5% and 20%.

키워드

참고문헌

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