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• 地质工程·环境工程 • 上一篇    下一篇

基于神经网络对有限元强度折减法分析

张晨,陈剑平,肖云华   

  1. 吉林大学 建设工程学院,长春 130026
  • 收稿日期:2008-09-20 修回日期:1900-01-01 出版日期:2009-01-26 发布日期:2009-01-26
  • 通讯作者: 张晨

Analysis on Theory of Strength Reduction FEM Based on Artificial Neural Networks

ZHANG Chen,CHEN Jian-ping,XIAO Yun-hua   

  1. College of Construction Engineering,Jilin University,Changchun 130026,China
  • Received:2008-09-20 Revised:1900-01-01 Online:2009-01-26 Published:2009-01-26
  • Contact: ZHANG Chen

摘要: 基于改进BP神经网络方法对目前较为流行的强度折减法的理论进行分析。人工神经网络可以模拟人脑的思维,可以在完全不知道数据分布形式和变量之间确切关系的的情况下处理边坡各参数之间的复杂非线性映射,利用这一优势预测分析不同的理论在实际应用中的准确性。将已经通过工程手段计算出结果的数据以输入单元、隐含层和输出单元的形式代入系统进行神经网络训练,不同的屈服准则对应训练出不同的网络系统。用训练好的网络对检验数据进行预测分析,还使用这一方法预测了不同剪胀角对边坡破坏的影响程度及趋势,结果显示,对于平面应变问题,在有限元强度折减法中使用DP4和DP5准则所得到的效果较好,DP1准则的误差最大。

关键词: 改进BP神经网络, 强度折减法, 屈服准则, 剪涨因素

Abstract: Application of theory on strength reduction FEM was analyzed based on improved BP neural networks. BP neural networks can simulate human brain and deal with complicated nonlinear relationship in different slope parameters under the condition of unknown relation between data distribution form and variables. Just taking the advantage the authors forecast the accuracy of every strength criterion in evaluating the slop stability. Take known data to the system to train the networks. According to different strength criterions, the different networks were trained. After that, the networks with new data were checked up. In the same way, the authors also analyze different slope damages considering character of dilatancy. The results show that using DP4 and DP5 criterions may obtain a favorable effect;meanwhile, the error is the largest in DP1 criterion. The conclusions would offer useful information for further application of strength reduction FEM.

Key words: improved BP neural networks, strength reduction FEM, strength criterion, character of dilatancy

中图分类号: 

  • P642
[1] 王科,王常明,王彬, 姚康,王天佐. 基于MorgensternPrice法和强度折减法的边坡稳定性对比分析[J]. 吉林大学学报(地球科学版), 2013, 43(3): 902-907.
[2] 周晓华, 林君, 陈祖斌, 焦健, 郭同健. 改进的神经网络反演微动面波频散曲线[J]. J4, 2011, 41(3): 900-906.
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