Earthquakes and Structures

  • 제18권2호
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  • Pages.163-170
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  • 2020
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  • 2092-7614(pISSN)
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  • 2092-7622(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Real-time seismic structural response prediction system based on support vector machine

  • Lin, Kuang Yi (Department of Civil Engineering, National Chiao Tung University) ;
  • Lin, Tzu Kang (Department of Civil Engineering, National Chiao Tung University) ;
  • Lin, Yo (Department of Civil Engineering, National Chiao Tung University)
  • 투고 : 2019.06.17
  • 심사 : 2019.11.26
  • 발행 : 2020.02.25
⟨ 이전 논문 다음 논문 ⟩

초록

Floor acceleration plays a major role in the seismic design of nonstructural components and equipment supported by structures. Large floor acceleration may cause structural damage to or even collapse of buildings. For precision instruments in high-tech factories, even small floor accelerations can cause considerable damage in this study. Six P-wave parameters, namely the peak measurement of acceleration, peak measurement of velocity, peak measurement of displacement, effective predominant period, integral of squared velocity, and cumulative absolute velocity, were estimated from the first 3 s of a vertical ground acceleration time history. Subsequently, a new predictive algorithm was developed, which utilizes the aforementioned parameters with the floor height and fundamental period of the structure as the new inputs of a support vector regression model. Representative earthquakes, which were recorded by the Structure Strong Earthquake Monitoring System of the Central Weather Bureau in Taiwan from 1992 to 2016, were used to construct the support vector regression model for predicting the peak floor acceleration (PFA) of each floor. The results indicated that the accuracy of the predicted PFA, which was defined as a PFA within a one-level difference from the measured PFA on Taiwan's seismic intensity scale, was 96.96%. The proposed system can be integrated into the existing earthquake early warning system to provide complete protection to life and the economy.

키워드

과제정보

연구 과제 주관 기관 : Ministry of Science and Technology

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피인용 문헌

  1. Reliability-based assessment of high-speed railway subgrade defect vol.77, pp.2, 2021, https://doi.org/10.12989/sem.2021.77.2.231
  2. Assessment and Localization of Structural Damage in r/c Structures through Intelligent Seismic Signal Processing vol.35, pp.9, 2021, https://doi.org/10.1080/08839514.2021.1935589