Mechanical Parameter Identification of Hydraulic Engineering with the Improved Deep Q-Network Algorithm
Mechanical Parameter Identification of Hydraulic Engineering with the Improved Deep Q-Network Algorithm
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DOI：
10.1155/2020/6404819
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发表时间：
2020-12-28
影响因子：
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通讯作者：
Li, Tongchun
中科院分区：
文献类型：
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作者：
Ji, Wei;Liu, Xiaoqing;Li, Tongchun
在长期的运行过程中，由于环境因素的影响，水工建筑物和地基的力学参数逐渐劣化。为了评估整体的安全性和耐久性，这些参数需要通过一些精确的分析方法来计算，这些方法受到计算效率和优化性能缓慢的阻碍。针对上述问题，提出了结合深度神经网络代理模型的改进深度Q网络算法。通过对坝体和实际工程地基不同分区的研究实例，表明本文改进的DQN算法在材料力学参数反演分析中具有良好的应用效果。
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.