Volume 14 Issue 4
Dec.  2021
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Article Navigation >  Water Science and Engineering  > 2021  >  14(4): 330-336
Hao Gu, Meng Yang, Chong-shi Gu, Xiao-fei Huang. 2021: A factor mining model with optimized random forest for concrete dam deformation monitoring. Water Science and Engineering, 14(4): 330-336. doi: 10.1016/j.wse.2021.10.004
Citation: Hao Gu, Meng Yang, Chong-shi Gu, Xiao-fei Huang. 2021: A factor mining model with optimized random forest for concrete dam deformation monitoring. Water Science and Engineering, 14(4): 330-336. doi: 10.1016/j.wse.2021.10.004
shu

A factor mining model with optimized random forest for concrete dam deformation monitoring

doi: 10.1016/j.wse.2021.10.004

  • a College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China;

  • b College of Agricultural Science and Engineering, Hohai University, Nanjing 210098, China;

  • c Library of Hohai University, Hohai University, Nanjing 210098, China

Funds:

This work was supported by the National Natural Science Foundation for Young Scientists of China (Grant No. 51909173), the State Key Program of National Natural Science of China (Grant No. 51739003), the Free Exploration Project of Hohai University (Grant No. B200201058), and the National Dam Safety Research Center (Grant No. CX2020B02).

  • Received Date: 2021-03-19
  • Accepted Date: 2021-10-12
    • Available Online: 2021-12-15
    Abstract
  • The unique structure of a dam complicates safety monitoring. Deformation can provide important information about dam evolution. In contrast to model prediction, actual dam response monitoring data can be used for diagnosis and early warning. Given the poor data mining ability of the conventional methods, it is essential to develop a method for extracting the factors influencing a dam. In this study, a data mining method and a model for evaluation of concrete dam deformation were developed using the evidence theory and a random forest. The model has the advantages of being easily understood, visualization with low complexity of training time, and accurate prediction. The model was applied to an actual concrete dam. The results indicated that the proposed random forest model could be used in analysis of concrete dams.

     

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