摘要
大坝变形性态是多种因素长期共同作用的结果,其演变模式包括时间和空间两个维度。然而,当前大坝变形智能建模较少综合考虑时空二维特征,原型观测资料中蕴含的大量时空信息亟待进一步挖掘。针对该问题,本文从单测点时序相关性和多测点空间关联性出发,提出构建一种耦合时空两个维度相关特性的大坝变形动态监控模型。该模型将门控循环单元(gated recurrent unit,GRU)神经网络作为核心层,建模学习历史变形数据内在时变规律,通过迭代提取有效变形因子来构造空间维度特征,并引入软注意力机制改进GRU隐藏状态的概率权重分配规则,实现对关键信息的自适应学习。以丰满混凝土重力坝多测点变形监测数据为例,验证了该模型的有效性。结果表明,所提出的监控模型能准确模拟大坝变形动态演变过程,且与常规监控模型相比,其外推预测精度更高,为大坝安全监控提供了新的方法和手段。
Dam deformation behavior is a consequence of long-term interaction of many factors,and its evolution pattern usually involves two dimensions:time and space.However,previous intelligent modeling of dam deformation lacks a comprehensive consideration of time and space variations,and a large amount of spatiotemporal information needs to be further excavated from the prototype observation data.This paper develops a dynamic monitoring model for dam deformation with spatiotemporal coupling correlation characteristics from two view angles:time-series correlation for a single measurement point,and spatial correlation of multiple measurement points.This model takes the gated recurrent unit(GRU)neural networks as core layers to model and learn the inherent time-varying patterns in a historical deformation data series,and constructs the features of spatial variations through iterative extraction of effective deformation factors.It uses a soft attention mechanism to improve the probability weight allocation rule of the GRU hidden states,thus achieving adaptive learning of key information.Its effectiveness is verified in a case study of the Fengman concrete gravity dam.The results show that this monitoring model can accurately simulate the dynamic deformation evolution of a dam,and are more accurate in extrapolation prediction than conventional monitoring models.
作者
任秋兵
李明超
沈扬
李明昊
REN Qiubing;LI Mingchao;SHEN Yang;LI Minghao(State Key Laboratory of Hydraulic Engineering Simulation and Safety,Tianjin University,Tianjin 300350;China Three Gorges Corporation,Beijing 100038)
出处
《水力发电学报》
CSCD
北大核心
2021年第10期160-172,共13页
Journal of Hydroelectric Engineering
基金
国家重点研发计划项目(2018YFC0406905)
国家自然科学基金面上项目(51879185)
湖北省水电工程施工与管理重点实验室开放基金(2020KSD06)。
关键词
大坝变形监控
时空相关特性
动态建模学习
门控循环单元神经网络
注意力机制
dam deformation monitoring
spatiotemporal correlation characteristics
dynamic modeling and learning
gated recurrent unit neural networks
attention mechanism
