Steel and Composite Structures

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A diagnostic approach for concrete dam deformation monitoring

  • Hao Gu (College of Water Conservancy and Hydropower Engineering, Hohai University) ;
  • Zihan Jiang (College of Water Conservancy and Hydropower Engineering, Hohai University) ;
  • Meng Yang (Nanjing Hydraulic Research Institute) ;
  • Li Shi (China Electric Construction Group Northwest Survey and Design Institute Company) ;
  • Xi Lu (China Electric Construction Group Northwest Survey and Design Institute Company) ;
  • Wenhan Cao (College of Water Conservancy and Hydropower Engineering, Hohai University) ;
  • Kun Zhou (College of Water Conservancy and Hydropower Engineering, Hohai University) ;
  • Lei Tang (Nanjing Hydraulic Research Institute)
  • Received : 2023.05.07
  • Accepted : 2023.08.01
  • Published : 2023.12.25
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Abstract

In order to fully reflect variation characteristics of composite concrete dam health state, the monitoring data is applied to diagnose composite concrete dam health state. Composite concrete dam lesion development to wreckage is a precursor, and its health status can be judged. The monitoring data are generally non-linear and unsteady time series, which contain chaotic information that cannot be characterized. Thus, it could generate huge influence for the construction of monitoring models and the formulation of corresponding health diagnostic indicators. This multi-scale diagnosis process is from point to whole. Chaotic characteristics are often contained in the monitoring data. If chaotic characteristics could be extracted for reflecting concrete dam health state and the corresponding diagnostic indicators will be formulated, the theory and method of diagnosing concrete dam health state can be huge improved. Therefore, the chaotic characteristics of monitoring data are considered. And, the extracting method of the chaotic components is studied from monitoring data based on fuzzy dynamic cross-correlation factor method. Finally, a method is proposed for formulating composite concrete dam health state indicators. This method can effectively distinguish chaotic systems from deterministic systems and reflect the health state of concrete dam in service.

Keywords

Acknowledgement

The research described in this paper was financially supported by National Natural Science Foundation of China (Grant No. 52379122); China Postdoctoral Science Foundation (Grant No. 2022M721668); the Fundamental Research Funds for the Central Universities of Hohai (Grant No. B230201011); Central Public-Interest Scientific Institution Basal Research Fund, NHRI (Y423006; Y423003); the Open Fund of Research Center on Levee Safety Disaster Prevention of Ministry of Water Re-sources under Grant(LSDP202204)Open Fund of Research Center on Levee Safety Disaster Prevention of Ministry of Water Resources under Grant (LSDP202204); Science and technology projects managed by the headquarter of State Grid Corporation (5108-202218280A-2-417-XG); the National Natural Science Foundation of China (51739008, 52239009, 52209159, U2243223, 51739003, 51739003; 51909173; 52109162); Open fund of National Dam Safety Research Center (Grant No. CX2020B02); Open Foundation of State Key Laboratory of Hydrology-Water Re-sources and Hydraulic Engineering (Grant No. 520003812), Fundamental Research Funds for the Central Universities (Grant No. 2019B69814). the Fundamental Research Funds for the Central Universities of Hohai (Grant No. B230201011); Water Conservancy Science and Technology Project of Jiangsu (Grant No. 2022024); The Fundamental Research Funds for the Central Universities (B210202017); the Jiangsu young science and technological talents support project (TJ-2022-076); Jiangsu Basic Research Program Natural Science Foundation Project (BK20221192); Anhui Natural Sci-ence Foundation grant number (Grant No. 2208085US17).

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