引用本文:陈一梅,范丽婵,李鑫.基于BP神经网络的丁坝坝头冲刷坑变化趋势预测[J].水利水运工程学报,2019,(6):125-131
【打印本页】   【HTML】   【下载PDF全文】   查看/发表评论  【EndNote】   【RefMan】   【BibTex】
←前一篇|后一篇→ 过刊浏览    高级检索
本文已被:浏览 49次   下载 29 本文二维码信息
码上扫一扫!
分享到: 微信 更多
基于BP神经网络的丁坝坝头冲刷坑变化趋势预测
陈一梅, 范丽婵, 李鑫
东南大学交通学院,江苏南京
摘要:
丁坝坝头冲刷坑的深度及位置变化影响坝基稳定,是丁坝安全评估的重要参数,且是一个复杂的非线性系统问题。以长江张南水道为研究对象,基于2008—2016年实测资料,分析来水来沙及其变化过程、冲刷坑位置和深度的变化,研究确定影响冲刷坑深度及位置变化的因子是河床边界条件(水深、河宽)、坝体属性(丁坝长度、挑角)及来水来沙因素(年径流量、年输沙量、不同级别来水来沙持续的天数),建立了基于BP神经网络的冲刷坑深度及位置变化趋势预测模型。计算结果表明,BP神经网络模型得到的冲刷坑深度和位置预测值与实测值较吻合,误差在(1.8~6.5)%,研究结果可为丁坝安全评估提供依据。
关键词:  丁坝  冲刷坑深度  冲刷坑位置  水沙特征  BP神经网络
DOI:
分类号:TV863
基金项目:国家重点研发计划资助项目(2018YFB1600400)
Prediction of the change trends of the scour near the head of the spur dike based on BP neural network
CHEN Yimei, FAN Lichan, LI Xin
School of Transportation, Southeast University, Nanjing
Abstract:
The scours maximum depth and position changes have a full influence on spur dikes safety, and are important parameters for evaluating the dikes safety. Taking the Zhangnan waterway on the Changjiang River as an example, this paper analyses the relationship between incoming flow, sediment and scours changes. The influencing factors of scours are the riverbed boundary conditions (water depth, river width), spur body properties (spur length, picking angle), incoming flow and incoming sand factors (annual runoff, annual sediment transport, days of different runoff and sediment transport). Based on the BP neural networks theory, a prediction model of the erosion changes near the spur is built. The result shows that the predicted values are approximate to the true one, which proves that this prediction model is feasible and effective. The result of this study can provide reference for the improvement of spur dikes safety assessment.
Key words:  spur dike  scours depth  scours position  characteristics of water and sediment  BP neural network
手机扫一扫看