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Research on Gross Error Detecting Method of Monitored Dam Deformation Data Based on Fully Convolutional Networks
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Zhi-yong QI1, Fu-ting SUN2, 3, Yan-pian MAO1, Jian-bo ZHOU2, Chun-hui ZHANG1, Qiu-yan LI1
Water Resources and Power | 2023, 41(3) : 87 - 90
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Water Resources and Power | 2023, 41(3): 87-90
• DAM SAFETY AND MONITORING •
Research on Gross Error Detecting Method of Monitored Dam Deformation Data Based on Fully Convolutional Networks
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Zhi-yong QI1, Fu-ting SUN2, 3, Yan-pian MAO1, Jian-bo ZHOU2, Chun-hui ZHANG1, Qiu-yan LI1
Affiliations
  • 1.China Yangtze Power Co., Ltd., Yichang 443002, China
  • 2.Large Dam Supervision Center, National Energy Administration of the People’s Republic of China, Hangzhou 311122, China
  • 3.PowerChina Huadong Engineering Co., Ltd., Hangzhou 311122, China
Published: 2023-03-25 doi: 10.20040/j.cnki.1000-7709.2023.20220749
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Goss error detecting method of monitored dam deformation data is researched based on the fully convolutional neural networks (FCN). Firstly, the method of representation learning of artificially labeled data sets by FCN model was proposed to simulate engineers’ experience. Secondly, the FCN model for gross error detection was built and artificially labeled data sets were used for model training. Finally, the trained FCN model was used for gross error detecting of monitored deformation data of a gravity dam. The results show that the gross error in monitored dam deformation data can be accurately obtained by the proposed method, which can improve efficiency of dam safety management.

monitoring  /  gross-error  /  FCN  /  dam safety  /  artificial intelligence
Zhi-yong QI, Fu-ting SUN, Yan-pian MAO, Jian-bo ZHOU, Chun-hui ZHANG, Qiu-yan LI. Research on Gross Error Detecting Method of Monitored Dam Deformation Data Based on Fully Convolutional Networks[J]. Water Resources and Power, 2023 , 41 (3) : 87 -90 . DOI: 10.20040/j.cnki.1000-7709.2023.20220749
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Year 2023 volume 41 Issue 3
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Article Info
doi: 10.20040/j.cnki.1000-7709.2023.20220749
  • Receive Date：2022-04-15
  • Online Date：2026-01-28
  • Published：2023-03-25
Article Data
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History
  • Received：2022-04-15
  • Revised：2022-06-06
Funding
Affiliations
    1.China Yangtze Power Co., Ltd., Yichang 443002, China
    2.Large Dam Supervision Center, National Energy Administration of the People’s Republic of China, Hangzhou 311122, China
    3.PowerChina Huadong Engineering Co., Ltd., Hangzhou 311122, China
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https://castjournals.cast.org.cn/joweb/sdnykx/EN/10.20040/j.cnki.1000-7709.2023.20220749
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