Smart Structures and Systems

  • 제33권1호
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  • Pages.17-26
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  • 2024
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

A long-term tunnel settlement prediction model based on BO-GPBE with SHM data

  • Yang Ding (Department of Civil Engineering, Hangzhou City University) ;
  • Yu-Jun Wei (Department of Civil Engineering, Zhejiang University) ;
  • Pei-Sen Xi (Zhejiang Engineering Research Center of Smart Rail Transportation, Power China Huadong Engineering Corporation Limited) ;
  • Peng-Peng Ang (Zhejiang Engineering Research Center of Smart Rail Transportation, Power China Huadong Engineering Corporation Limited) ;
  • Zhen Han (Nanjing Metro Operation Co., Ltd.)
  • 투고 : 2023.07.29
  • 심사 : 2023.12.01
  • 발행 : 2024.01.25
⟨ 이전 논문 다음 논문 ⟩

초록

The new metro crossing the existing metro will cause the settlement or floating of the existing structures, which will have safety problems for the operation of the existing metro and the construction of the new metro. Therefore, it is necessary to monitor and predict the settlement of the existing metro caused by the construction of the new metro in real time. Considering the complexity and uncertainty of metro settlement, a Gaussian Prior Bayesian Emulator (GPBE) probability prediction model based on Bayesian optimization (BO) is proposed, that is, BO-GPBE. Firstly, the settlement monitoring data are analyzed to get the influence of the new metro on the settlement of the existing metro. Then, five different acquisition functions, that is, expected improvement (EI), expected improvement per second (EIPS), expected improvement per second plus (EIPSP), lower confidence bound (LCB), probability of improvement (PI) are selected to construct BO model, and then BO-GPBE model is established. Finally, three years settlement monitoring data were collected by structural health monitoring (SHM) system installed on Nanjing Metro Line 10 are employed to demonstrate the effectiveness of BO-GPBE for forecasting the settlement.

키워드

과제정보

This paper was supported by the Ministry of education of Humanities and Social Science project (No. 23YJCZH037), the Foundation of the State Key Laboratory of Mountain Bridge and Tunnel Engineering (No. SKLBT2210), China, the Educational Science Planning Project of Zhejiang Province (No. 2023SCG222), and the Scientific Research Project of Zhejiang Provincial Department of Education (No. Y202248682).

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