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Intelligent Optimization Radial Basis Function Network Model for Multi-parameter Inversion of Super-high Arch Dam
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Wen-zhuang KOU1, Fei KANG1, Zhi MEI2
Water Resources and Power | 2023, 41(11) : 69 - 72
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Water Resources and Power | 2023, 41(11): 69-72
• DAM SAFETY AND MONITORING •
Intelligent Optimization Radial Basis Function Network Model for Multi-parameter Inversion of Super-high Arch Dam
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Wen-zhuang KOU1, Fei KANG1, Zhi MEI2
Affiliations
  • 1.School of Hydraulic Engineering, Dalian University of Technology, Dalian 116023, China
  • 2.PowerChina Zhongnan Engineering Corporation Limited, Changsha 410014, China
Published: 2023-11-25 doi: 10.20040/j.cnki.1000-7709.2023.20230213
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Accurate inversion of dam mechanical parameters based on dam monitoring data is crucial to ensure the safe and stable operation of the dam. This paper presented an arch dam parameter inversion model based on radial basis function (RBF) network and Artificial Gorilla Troops Optimizer (GTO). Firstly, the RBF surrogate model was used to replace the finite element model to discuss the relationship between the material parameters and the displacement response of the monitoring point. The sampling data of the RBF surrogate model was generated by the efficient Latin hypercube sampling technology. Secondly, the GTO intelligent optimization algorithm was adopted to minimize the objective function of material parameter identification. The analysis results of engineering examples show that the RBF-GTO model can achieve high-precision parameter inversion analysis of concrete super-high arch dams while reducing the calculation cost.

radial basis function network  /  surrogate model  /  displacement back analysis  /  intelligent optimization  /  super-high arch dam
Wen-zhuang KOU, Fei KANG, Zhi MEI. Intelligent Optimization Radial Basis Function Network Model for Multi-parameter Inversion of Super-high Arch Dam[J]. Water Resources and Power, 2023 , 41 (11) : 69 -72 . DOI: 10.20040/j.cnki.1000-7709.2023.20230213
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Year 2023 volume 41 Issue 11
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Article Info
doi: 10.20040/j.cnki.1000-7709.2023.20230213
  • Receive Date：2023-02-17
  • Online Date：2026-01-27
  • Published：2023-11-25
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History
  • Received：2023-02-17
  • Revised：2023-03-22
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    1.School of Hydraulic Engineering, Dalian University of Technology, Dalian 116023, China
    2.PowerChina Zhongnan Engineering Corporation Limited, Changsha 410014, China
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