Earthquakes and Structures

  • 제22권4호
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  • Pages.401-419
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  • 2022
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  • 2092-7614(pISSN)
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  • 2092-7622(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

A novel liquefaction prediction framework for seismically-excited tunnel lining

  • Shafiei, Payam (Department of Civil Engineering, Qazvin Branch, Islamic Azad University) ;
  • Azadi, Mohammad (Department of Civil Engineering, Qazvin Branch, Islamic Azad University) ;
  • Razzaghi, Mehran Seyed (Department of Civil Engineering, Qazvin Branch, Islamic Azad University)
  • 투고 : 2021.09.21
  • 심사 : 2022.04.22
  • 발행 : 2022.04.25
⟨ 이전 논문 다음 논문 ⟩

초록

A novel hybrid extreme machine learning-multiverse optimizer (ELM-MVO) framework is proposed to predict the liquefaction phenomenon in seismically excited tunnel lining inside the sand lens. The MVO is applied to optimize the input weights and biases of the ELM algorithm to improve its efficiency. The tunnel located inside the liquefied sand lens is also evaluated under various near- and far-field earthquakes. The results demonstrate the superiority of the proposed method to predict the liquefaction event against the conventional extreme machine learning (ELM) and artificial neural network (ANN) algorithms. The outcomes also indicate that the possibility of liquefaction in sand lenses under far-field seismic excitations is much less than the near-field excitations, even with a small magnitude. Hence, tunnels designed in geographical areas where seismic excitations are more likely to be generated in the near area should be specially prepared. The sand lens around the tunnel also has larger settlements due to liquefaction.

키워드

참고문헌

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