摘要
为了提高大坝变形的预测精度,采用小波变换和分形理论对大坝位移观测数据的非线性动力学特性进行了分析,揭示了其具有低维混沌动力特性,这为大坝变形预测模型的建立提供了理论依据和先验知识。基于低维混沌动力特性,设计了能捕获大坝位移观测数据全局动力特性,兼具神经网络模型结构优化和动力机制时新的混沌优化神经网络大坝变形预测模型。在工程实例中,由多个度量指标组成量化评价体系,对模型预测性能进行综合评价,结果表明,所建模型比传统BP神经网络和ARMA模型具有更高的预测精度。
Dam deformation prediction is important for dam safety monitoring and has become a focus of increasing interest in recent years. In this study, on the basis of nonlinear dynamic property analysis of the observations of dam displacements, a novel methodology is proposed to establish dam deformation prediction model with improved prediction precision. Firstly, the dynamic properties of observations of dam displacements are studied by combined wavelet transform with fractal, and the results reveal that dam displacements possess certain low dimensional chaotic character. This provides theoretical foundation and transcendental knowledge for relational establishment of dam deformation prediction model. Moreover, derived from the low dimensional chaotic character, a chaos-optimized neural network model for dam deformation prediction is constructed, which is not only capable of capturing the dynamic properties of observations of dam displacements but also of implementing the model＇s structural optimization and dynamic mechanism refreshing. Finally, in the practical application of dam deformation prediction, the model performance is quantificationally assessed by multiple indices. The result demonstrates that chaos-optimized neural network model holds higher prediction precision than the conventional back propagation （BP） neural network and ARMA models; and therefore, it is promising for dam safety monitoring.
出处
《岩土力学》
EI
CAS
CSCD
北大核心
2006年第8期1344-1348,共5页
Rock and Soil Mechanics
基金
中国博士后科学基金资助项目(No.2005038236)
国家自然科学基金资助项目(No.50379005)
关键词
大坝位移
低维混沌
动力特性
小波变换
混沌优化神经网络
dam displacements
low dimensional chaos
dynamic properties
wavelet transform
chaotic-optimimal neural networks
