欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
大坝蓄水初期监测资料有限且波动性大,坝体结构处于适应变形的危险阶段。基于这种情况,结合灰色理论、神经网络和加权马尔可夫链理论的优点,构建灰色神经网络-加权马尔可夫链的大坝变形监控模型并对某碾压混凝土坝蓄水初期水平位移进行预测。结果表明,该模型解决了样本数据少、波动性大的问题,拟合效果较好,预测精度较高。
英文摘要：
The dam monitoring data is limited and the structure adapts to the risk of deformation at the early impoundment stage. In such a situation, by means of the Grey Theory, Neural Networks and Weighted Markov Chain Theory, a Dam Deformation Monito- ring Model based on Gray neural network -- Weighted Markov Chain is built to predict a RCC dam horizontal displacement at the early impoundment stage. Prediction results show that this model solves the problem of fewer data and greater deformation data vola- tility to achieve good fitting and precision.
同期刊论文项目
同项目期刊论文
Blind source separation model of earth-rock junctions in dike engineering based on distributed optic
Evaluation of various temperature control schemes for crack prevention in RCC arch dams during const
Derivation Method for the Foundation Boundaries of Hydraulic Numerical Simulation Models Based on th
Analysis and back-analysis for temperature field of concrete arch dam during construction period bas
Rate effect on mechanical properties of hydraulic concrete flexural-tensile specimens under low load
A Monitoring Model for High Slope Displacement considering the Effects of Dynamic Structure Mutation
期刊信息
位置：