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Mechanical Parameter Identification of Hydraulic Engineering with the Improved Deep Q-Network Algorithm

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  • Wei Ji
  • Xiaoqing Liu
  • Huijun Qi
  • Xunnan Liu
  • Chaoning Lin
  • Tongchun Li

Abstract

During the long-term operating period, the mechanical parameters of hydraulic structures and foundation deteriorated gradually because of the environmental factors. In order to evaluate the overall safety and durability, these parameters should be calculated by some accurate analysis methods, which are hindered by slow computational efficiency and optimization performance. The improved deep Q-network (DQN) algorithm combined with the deep neural network (DNN) surrogate model was proposed in this paper to ameliorate the above problems. Through the study cases of different zoning in the dam body and the actual engineering foundation, it is shown that the improved DQN algorithm has a good application effect on inversion analysis of material mechanical parameters in this paper.

Suggested Citation

  • Wei Ji & Xiaoqing Liu & Huijun Qi & Xunnan Liu & Chaoning Lin & Tongchun Li, 2020. "Mechanical Parameter Identification of Hydraulic Engineering with the Improved Deep Q-Network Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-20, December.
    • Handle: RePEc:hin:jnlmpe:6404819
    DOI: 10.1155/2020/6404819
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    1. Sercan Yalçın & Münür Sacit Herdem, 2024. "Optimizing EV Battery Management: Advanced Hybrid Reinforcement Learning Models for Efficient Charging and Discharging," Energies, MDPI, vol. 17(12), pages 1-21, June.

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