›› 2016, Vol. 37 ›› Issue (S2): 649-657.doi: 10.16285/j.rsm.2016.S2.082

• 岩土工程研究 • 上一篇    下一篇

基于Alpha稳定分布概率神经网络的围岩稳定性分类研究

王佳信,周宗红,赵 婷,余洋先,龙 刚,李春阳   

  1. 昆明理工大学 国土资源工程学院,云南 昆明 650093
  • 收稿日期:2016-07-05 出版日期:2016-11-11 发布日期:2018-06-09
  • 通讯作者: 周宗红,男,1967年生,博士,教授,主要从事岩石力学方面的研究与教学工作。E-mail: Zhou20051001@ 163.com E-mail:949147232@qq.com
  • 作者简介:王佳信,男,1988年生,硕士研究生,主要从事采矿技术及岩土工程方面的研究工作
  • 基金资助:
    国家自然科学基金(No. 51264018, No. 51064012)

Application of Alpha stable distribution probabilistic neural network to classification of surrounding rock stability assessment

WANG Jia-xin, ZHOU Zong-hong, ZHAO Ting, YU Yang-xian, LONG Gang, LI Chun-yang   

  1. Faculty of Land Resources Engineering,Kunming University of Science and Technology, Kunming, Yunnan 650093, China
  • Received:2016-07-05 Online:2016-11-11 Published:2018-06-09
  • Supported by:
    This work was supported by National Natural Science Foundation of China (51264018, 51064012).

摘要: 工程围岩是一种高度非线性的复杂动态系统,其影响因素众多,单一的评价指标已不能准确描述围岩分类情况。目前,综合考虑多种指标评价围岩分类的方法很多,但围岩评价指标之间或多或少存在一定的相关性,其评价指标中存在一些服从非高斯分布的指标,无法满足概率神经网络(PNN)样本层中采用高斯分布作径向基函数的要求,因此,提出一种对称Alpha稳定分布(SaS)。SaS有更广泛的数学表达,其径向对称特性还可充当PNN样本层中高斯分布。在SaS的基础上,建立广州抽水蓄能电站二期工程围岩分类评价的SaS-PNN模型。预测结果表明,SaS-PNN模型具有良好的预测效果,其误判率为为4.55%。可为地下工程围岩分类评价提供一种新思路。

关键词: Alpha稳定分布(S?S), 概率神经网络, 围岩稳定性, 分类

Abstract: Engineering surrounding rock is a highly nonlinear complex dynamic system, and its influence factors are so numerous that single evaluation index can accurately not describe the surrounding rock classification. At present,there are many methods to evaluate surrounding rock classification via considering various indicators together. However,the correlations exist in these evaluation indexes more or less. That is to say,these evaluation indexes are indicators of non-Gaussian distribution; so it is difficult to meet the request that sample layers of probabilistic neural network(PNN) must adopt Gaussian distribution as radial basis function. Given this,we presented a symmetric stable distribution (SaS) which not only has a wider range of mathematical expression than the Gauss distribution,but also it could act as the radial basis function of sample beds in probabilistic neural network (PNN). The model of the surrounding rock classification evaluation in the second-stage project of Guangzhou pumped storage power station can be established based on the SaS distribution. The predicted results show that the model has a good effect and its false-positive rate is 4.55%. This model can provide a new way for predicting the effect of surrounding rock classification in underground engineering.

Key words: Alpha stable distribution(S?S), probabilistic neural network, stability of surrounding rock, classification

中图分类号: 

  • TU 452
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