特高拱坝运行初期坝前库水温垂直分层逐步形成,坝体混凝土持续水化内部温度回升。传统变形预测模型采用的周期项温度因子不能很好地描述运行初期环境和坝体内部温度的非线性非稳定性特征。对此,融合主成分分析和分层聚类方法,提出了基于主成分分层聚类法的运行初期实测环境和坝体温度因子分类分区及典型测点的选取方法;同时,引入了可反映周期性库水位变化下运行初期谷幅收缩变形特征的指数和周期项的组合时效因子;在此基础上,构建了基于实测温度因子的多元回归模型和支持向量机模型。实例分析表明,选取的实测温度因子能较好地反映运行初期坝体温度的时空变化特征,以此构建的模型比传统模型具有更高的预测精度。
Abstract
The water temperature in front of super-high arch dam gradually stratifies vertically in the initial operation period, resulting in the rebound of internal temperature of the dam concrete. Periodic terms of temperature effect used in conventional deformation prediction models could not well describe the nonlinearity and nonstationarity of environmental and internal temperatures of dam during its initial operation. In view of this, a method of classifying measured ambient and dam temperatures and selecting typical measurement points is presented by integrating principal component analysis and hierarchical clustering. Meanwhile,a combination of time varying effects, including both index term and cycle term,that reflects the valley deformation under the periodic fluctuation of reservoir water level during the initial operation is introduced. On this basis,a multivariate regression model and a support vector machine model based on measured temperature variable are constructed. Case study demonstrates that the selected measured temperature variable well reflects the spatio-temporal characteristics of dam temperature field during initial operation, and the corresponding constructed model is of higher prediction accuracy than traditional models.
关键词
特高拱坝 /
运行初期 /
变形 /
温度因子 /
预测模型 /
主成分分析 /
分层聚类法 /
支持向量机
Key words
super-high arch dam /
initial operation period /
deformation /
temperature factor /
prediction model /
principal component analysis /
clustering /
support vector machine
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基金
国家重点研发计划项目(2018YFC0406705);国家自然科学基金面上项目(51879169,51779155)
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