Uranium Tailings Dam Instability prediction Analysis Based On RBF Neural Network Method
Tailings dam is an important part of the mining industry facilities, The safety and stable operation of the tailings dam for concentrator production plays a very important role, so it is very important to monitoring the stability of tailings dam. This article uses RBF neural network to predict the sample data and compare with the actual value for tailings dam water level, and it shows predicted and actual values are very close, and the error is small. Meanwhile, the network training repeated iteration and the error curve converges to the target. In summary explanation, the training effect is good, and it can be used to predict other parameters. The prediction results shows the water level elevation error is in the 0.001m, it belongs to a reasonable extent, the water level is also in a safe state, and it’s risk is lower.
The stability of the dam RBF neural network uranium tailings dam reservoir water level
Zan Guo Shuliang Zou Yong Liu Dewen Tang
University of South China, Hunan Provincial Key Laboratory of Emergency Safety Technology and Equipm University Of South China, 421001, Hengyang, P.R. China
国际会议
西安
英文
352-357
2016-05-25（万方平台首次上网日期，不代表论文的发表时间）