摘要
随着大坝建设中安全问题的不断出现,安全监测的重要性不容忽视。监测数据分析总结和超前预报是提前决策的重要基础,将直接影响整个大坝监测的成效。文章基于蚁群算法优化BP神经网络,结合实际工程建立了ACO-BP神经网络模型,计算成果表明在大坝安全监测中,该模型收敛速度更快,预测精度更高,可以在大坝的安全监测中推广使用。
With constant occurrence of the safety issues about dam construction,the importance of safety monitoring can’t be ignored. To analyze and conclude the monitoring data,or advanced prediction,is the important basis to make decisions ahead of time,which will affect directly the effect of the whole dam monitoring. Based on the ant colony algorithm,this study optimized the BP neural network,combined with the practical engineering, to establish the ACO-BP neural network model. The calculation results show that this model converges at a faster rate in dam safety monitoring with high forecasting accuracy,may be applied in dam safety monitoring.
作者
刘朝利
LIU Chao-li(Fujin City Water Affairs Bureau,Fujin 156100,China)
出处
《黑龙江水利科技》
2018年第11期5-8,共4页
Heilongjiang Hydraulic Science and Technology
关键词
神经网络
土石坝变形
案例监测
测点
neural network
earth-rock dam displacement
safety monitoring
observation point
作者简介
刘朝利(1974-),男,黑龙江富锦人,高级工程师,从事水利工程管理工作。
