Earthquakes and Structures

  • 제14권2호
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  • Pages.143-153
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  • 2018
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  • 2092-7614(pISSN)
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  • 2092-7622(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Copula entropy and information diffusion theory-based new prediction method for high dam monitoring

  • Zheng, Dongjian (State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University) ;
  • Li, Xiaoqi (State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University) ;
  • Yang, Meng (State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University) ;
  • Su, Huaizhi (State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University) ;
  • Gu, Chongshi (State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University)
  • 투고 : 2017.06.06
  • 심사 : 2018.01.30
  • 발행 : 2018.02.25
⟨ 이전 논문 다음 논문 ⟩

초록

Correlation among different factors must be considered for selection of influencing factors in safety monitoring of high dam including positive correlation of variables. Therefore, a new factor selection method was constructed based on Copula entropy and mutual information theory, which was deduced and optimized. Considering the small sample size in high dam monitoring and distribution of daily monitoring samples, a computing method that avoids causality of structure as much as possible is needed. The two-dimensional normal information diffusion and fuzzy reasoning of pattern recognition field are based on the weight theory, which avoids complicated causes of the studying structure. Hence, it is used to dam safety monitoring field and simplified, which increases sample information appropriately. Next, a complete system integrating high dam monitoring and uncertainty prediction method was established by combining Copula entropy theory and information diffusion theory. Finally, the proposed method was applied in seepage monitoring of Nuozhadu clay core-wall rockfill dam. Its selection of influencing factors and processing of sample data were compared with different models. Results demonstrated that the proposed method increases the prediction accuracy to some extent.

키워드

과제정보

연구 과제 주관 기관 : National Natural Science Foundation of China

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