摘要
高精度的变形预测对大坝安全运行和长期维护具有重要意义。针对当下方法预测精度低、数据信息挖掘不充分的问题,通过将变形序列进行变分模态分解,构建大坝变形影响因子与分量之间的关系,进而搭建不同结构参数的长短期记忆神经网络,最终提出了一种大坝变形分析模型。该模型综合灰狼算法、最小能量误差标准、最小冗余最大相关性方法等策略,从前端分解、信息提取和时序预测三方面对模型进行了改进,实现了最优化建模。实例分析表明,相较于常规监控模型,所提出的变形分析模型能够准确模拟坝体形变过程,具有较高的预测精度和泛化性能,可以为大坝变形安全分析提供参考。
Accurate deformation prediction is of great significance to safe operation and long-term maintenance of dams,but previous methods have low prediction accuracy and lack sufficient information extraction from monitoring data.This paper constructs a relationship of dam deformation components versus their influencing factors through variational mode decomposition on the deformation series,and constructs Long Short-Term Memory neural networks with different structural parameters.Then,we develop a dam deformation analysis model that can realize optimal modeling through integrating the Grey Wolf Optimizer algorithm,the Minimum Redundancy Maximum Relevance method,and other strategies to improve its accuracy from three aspects-front-end decomposition,information extraction,and time series prediction.A case study shows that compared with the conventional monitoring model,this new model is more accurate in the simulations of dam deformation time variations and better in generalization performance,thus useful for dam deformation safety analysis.
作者
漆一宁
苏怀智
姚可夫
杨佳泉
徐伟男
QI Yining;SU Huaizhi;YAO Kefu;YANG Jiaquan;XU Weinan(State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering,Hohai University,Nanjing 210098,China;College of Water Conservancy and Hydropower Engineering,Hohai University,Nanjing 210098,China;Huishan District Water Conservancy Bureau,Wuxi 214000,China)
出处
《水力发电学报》
CSCD
北大核心
2023年第7期56-68,共13页
Journal of Hydroelectric Engineering
基金
国家自然科学基金项目(52239009
51979093)。
关键词
大坝变形监控模型
特征筛选
变分模态分解
长短期记忆神经网络
最小冗余最大相关性
dam deformation monitoring model
feature screening
variational mode decomposition
Long Short-Term Memory neural network
Minimum-Redundancy Maximum-Relevance
