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

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Publicado en:Mathematical Problems in Engineering vol. 2020 (2020)
Autor principal: Ji, Wei
Otros Autores: Liu, Xiaoqing, Qi, Huijun, Liu, Xunnan, Lin, Chaoning, Li, Tongchun
Publicado:
John Wiley & Sons, Inc.
Materias:
Dams
Machine learning
Parameter identification
Accuracy
Deep learning
Hydraulic engineering
Concrete
Artificial neural networks
Optimization
Neural networks
Mechanical properties
Dam engineering
Algorithms
Engineering
Methods
Heuristic
Hydraulics
Hydraulic structures
Acceso en línea:Citation/Abstract
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Resumen: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.
ISSN:1024-123X
1563-5147
DOI:10.1155/2020/6404819
Fuente:Advanced Technologies & Aerospace Database