Structural Monitoring and Maintenance

  • 제10권2호
  • /
  • Pages.175-190
  • /
  • 2023
  • /
  • 2288-6605(pISSN)
  • /
  • 2288-6613(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Structural monitoring and maintenance by quantitative forecast model via gray models

  • C.C. Hung (Faculty of National Hsin Hua Senior High School) ;
  • T. Nguyen (Ha Tinh University)
  • 투고 : 2023.04.07
  • 심사 : 2023.06.05
  • 발행 : 2023.06.25
⟨ 이전 논문 다음 논문 ⟩

초록

This article aims to quantitatively predict the snowmelt in extreme cold regions, considering a combination of grayscale and neural models. The traditional non-equidistant GM(1,1) prediction model is optimized by adjusting the time-distance weight matrix, optimizing the background value of the differential equation and optimizing the initial value of the model, and using the BP neural network for the first. The adjusted ice forecast model has an accuracy of 0.984 and posterior variance and the average forecast error value is 1.46%. Compared with the GM(1,1) and BP network models, the accuracy of the prediction results has been significantly improved, and the quantitative prediction of the ice sheet is more accurate. The monitoring and maintenance of the structure by quantitative prediction model by gray models was clearly demonstrated in the model.

키워드

참고문헌

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