...
首页> 外文期刊>Computer Assisted Mechanics and Engineering Sciences >Application of artificial neural network in soil parameter identification for deep excavation numerical model
【24h】

Application of artificial neural network in soil parameter identification for deep excavation numerical model

机译:人工神经网络在深基坑数值模型土性参数识别中的应用

获取原文
获取原文并翻译 | 示例
           

摘要

In this paper, an artificial neural network (ANN) is used to approximate response of deep excavation numerical model on input parameters. The approximated model is then used in minimization procedure of the inverse problem, i.e. minimization of the differences between the response of the model (now, neural network) and the field measurements. ANN based objective function is continuous and differentiable thus gradient based optimization algorithm can be efficiently used in this problem. It is showed that initial approximation of the numerical model by means of ANN increase efficiency of the identification process without loss of accuracy.
机译:本文使用人工神经网络(ANN)来估算深基坑数值模型对输入参数的响应。然后将近似模型用于反问题的最小化过程,即,最小化模型的响应(现在是神经网络)与现场测量之间的差异。基于ANN的目标函数是连续且可微的,因此基于梯度的优化算法可以有效地解决此问题。结果表明,通过人工神经网络对数值模型进行初始逼近可以提高识别过程的效率,而不会降低精度。

著录项

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