• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Mathematical Problems in Engineering >Inverse Parametric Analysis of Seismic Permanent Deformation for Earth-Rockfill Dams Using Artificial Neural Networks
【24h】

Inverse Parametric Analysis of Seismic Permanent Deformation for Earth-Rockfill Dams Using Artificial Neural Networks

机译：基于人工神经网络的土石坝大坝永久变形反参数分析

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This paper investigates the potential application of artificial neural networks in permanent deformation parameter identification for rockfill dams. Two kinds of neural network models, multilayer feedforward network (BP) and radial basis function (RBF) networks, are adopted to identify the parameters of seismic permanent deformation for Zipingpu Dam in China. The dynamic analysis is carried out by three-dimensional finite element method, and earthquake-induced permanent deformation is calculated by an equivalent nodal force method. Based on the sensitivity analysis of permanent deformation parameters, an objective function for network training is established by considering parameter sensitivity, which can improve the accuracy of parameter identification. By comparison, it is found that RBF outperforms the BP network in this problem. The proposed inverse analysis model for earth-rockfill dams can identify the seismic deformation parameters with just a small amount of sample designs, and much calculation time can be saved by this method.
机译：本文研究了人工神经网络在堆石坝永久变形参数识别中的潜在应用。采用多层前馈网络（BP）和径向基函数（RBF）网络这两种神经网络模型来识别中国紫坪铺大坝的地震永久变形参数。通过三维有限元方法进行动力分析，并通过等效节点力法计算地震引起的永久变形。在永久变形参数敏感性分析的基础上，考虑参数敏感性建立了网络训练目标函数，可以提高参数识别的准确性。通过比较，发现在该问题中，RBF优于BP网络。提出的土石坝反分析模型只需少量的样例设计即可识别出地震变形参数，该方法可节省大量的计算时间。

著录项

  • 来源
    《Mathematical Problems in Engineering》 |2012年第12期|383749.1-383749.19|共19页
  • 作者

    Xu Wang; Fei Kang; Junjie Li; Xin Wang;

  • 作者单位

    Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian, Liaoning 116024, China;

    Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian, Liaoning 116024, China;

    Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian, Liaoning 116024, China;

    College of Computer Science and Technology, Zhejinng University of Technology, Hangzhou, Zhejiang 310023, China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号