Print Email Facebook Twitter Mechanical Parameter Identification of Hydraulic Engineering with the Improved Deep Q-Network Algorithm Title Mechanical Parameter Identification of Hydraulic Engineering with the Improved Deep Q-Network Algorithm Author Ji, Wei (Hohai University) Liu, Xiaoqing (Hohai University) Qi, Huijun (Hohai University) Liu, Xunnan (Hohai University) Lin, C. (TU Delft Safety and Security Science; Hohai University) Li, Tongchun (Hohai University) Date 2020 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. To reference this document use: http://resolver.tudelft.nl/uuid:07ae2d3e-67f9-4a38-89b9-5308a17a241c DOI https://doi.org/10.1155/2020/6404819 ISSN 1024-123X Source Mathematical Problems in Engineering: theory, methods and applications, 2020 Part of collection Institutional Repository Document type journal article Rights © 2020 Wei Ji, Xiaoqing Liu, Huijun Qi, Xunnan Liu, C. Lin, Tongchun Li Files PDF 6404819.pdf 2.9 MB Close viewer /islandora/object/uuid:07ae2d3e-67f9-4a38-89b9-5308a17a241c/datastream/OBJ/view