土工基础

›› 2012, Vol. 26 ›› Issue (4): 85-87.

• 工程实录 • 上一篇    下一篇

BP神经网络在灰坝动力计算中的应用

陈建斌1,何刚雁2,范伟1   

  1. 1.武汉市政工程设计研究院有限责任公司，武汉 430023；2.武汉市城建基金办污水全收集全处理项目建设管理办公室委员会,武汉 430050
  • 收稿日期:2011-10-13 出版日期:2012-08-20 发布日期:2012-09-07
  • 作者简介:陈建斌(1974-)，男，湖北武汉人，博士，高级工程师，研究方向为岩土工程设计和技术。

Application of BP Neural Network in the Dynamic Calculation of Flyash Dam

CHEN Jianbin1, HE Gangyan2, FAN Wei1   

  1. 1.Wuhan Municipal Engineering Design and Research Institute Co.Ltd, Wuhan 430023;

     2.Office of Waste Water Collection and Treatment Project, Wuhan Municipal Construction Foundation, Wuhan 430050
  • Received:2011-10-13 Online:2012-08-20 Published:2012-09-07

摘要： 针对灰坝动力计算模型参数的不确定性和复杂性，以大量室内动力试验所得参数为预测样本，以粉煤灰的基本物理特性参数作为输入值，利用BP神经网络强大的非线性映射能力，建立了灰坝动力本构方程参数的预测模型。将其应用于某电厂灰坝的动力有限元计算中，计算所得灰坝的模态参数、动力响应和破坏性态与模型试验结果进行分析比较，取得了比较一致的结论。最后提出了一些对灰坝抗震有价值的建议。

关键词: 灰坝, BP神经网络, 预测模型, 动力有限元

Abstract: Considering the uncertainty and complexity of dynamic parameters of a computational model used in the flyash dam simulation, a predictive model of the dynamic parameters of ash dam simulation is established. The sampling pool used in the predictive model is obtained from laboratory tested soil parameters. The model also adopts the predictability of the Back Propagation (BP) neural network. The dynamic response of a flyash dam of a power plant was analyzed and the numerical results were compared with the measured results. This analytical approach provides useful recommendations for the response of a flyash dam under a seismic condition.

Key words: Flyash Dam, Back Propagation (BP) Neural Network, Predictive Model, dynamic FEM