Artificial Neural Network–Based Feedback Analysis of the Caverns Excavation of Underground Hydropower Plants
The monitoring feedback analysis with measured data such as displacements, stresses and supporting forces is a common approach to appraise or predict the safety of a project in use or during construction. In this paper, the three-dimensional computer code for fast Lagrangian analysis of continua, FLAC3D, with the explicit finite difference method are used in the numerical simulation, while the artificial neural network (ANN) is exploited to back analyze the material parameters adopted in the final computation. The feedback analysis of the excavation of the underground caverns of Xiluodu hydropower station on Jinsha River in southwest China is conducted as an engineering application. The implementation process of this method is introduced in detail. The process can feed back and forecast displacements, stresses and supporting forces being interested in the plan and construction of underground power stations. It is shown by this paper that the ANN-based feedback analysis method is effective and feasible.
Feedback analysis Artificial neural network Explicit finite difference method Underground hydropower plants FLAC3D
Ming Zhang Liang Chen Zhongkui Li
State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, P. R. China
国际会议
香港
英文
1-4
2009-05-19（万方平台首次上网日期，不代表论文的发表时间）