基于神经网络的铀矿边坡稳定性分析

Yufeng Zhu, X. Ding, Zhi‐wei Li, Shijian Zhou
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引用次数: 3

摘要

如何准确预测滑坡的发生,已成为矿山开采过程中的难题之一。本文简要介绍了人工神经网络和BP网络模型,并利用福州金安铀业有限公司边坡稳定性研究和预测样本,分析了参数选择、数据采集、处理和网络构成等重要问题,建立了预测模型。本文讨论了基于BP神经网络的边坡稳定方法及其有效性。算例表明,通过训练样本的检验,利用人工神经网络逼近对边坡稳定性的预测可以取得满意的结果。该模型为今后边坡稳定性评价提供了一种可行的方法。同时,也证明了神经网络在矿山边坡稳定性分析中的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis on Uranic Slope Stability Based on Neural Network
How to accurately predict the occurrence of landslides, and it has become one of the troubles in the mining process. The author made a brief introduction of artificial neural network and BP network model in this paper, and also analysis some important problems, such as the parameters selecting, data collecting, processing and network constituting by using the study and forecast samples which from the slope stability of Fuzhou Jin-An Uranium Industry Limited Company, and then setting a prediction model. This paper discusses the slope stability methods and its effectiveness based on the BP neural network. Examples of calculation shows that the using of artificial neural network approaching to the stability of the slope of the forecast can made satisfactory results through the training sample test. This model provides a viable method for the future stability of the slope of such evaluation. At the same time, the feasibility of the application for the neural network in the mine slope stability is proved.
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