改进的特高拱坝分区变形预测模型

Improved zonal deformation prediction model for super-high arch dams

已有特高拱坝分区变形预测模型不能辨识趋势性、周期性和波动性的空间差异性。提出一种改进的模型,应用变分模态分解将变形分解为趋势性、周期性和波动性分量;通过基于主成分的分层聚类方法确定温度因子,将库水位、温度和谷幅等影响因子分解为趋势性、低频和高频成分。采用基于形状距离的优化动态时间规整算法进行变形分量分区,计算分区质心序列,利用灰色关联度确定主要影响因子成分。构建基于实测温度的统计模型以及随机森林、最小二乘支持向量机和增强回归树等算法的分区预测模型,叠加各分量得到变形预测值。利用溪洛渡特高拱坝运行初期的变形数据验证改进分区模型的有效性,结果表明,模型精度较高,且同时识别了位移的空间关联性和差异性。

Previous zonal deformation prediction models lack the capability of capturing spatial differences in the trend,periodic and fluctuating components of dam deformation.This paper describes an improved zonal deformation prediction model to solve this problem.First,we adopt a variational mode decomposition algorithm to split dam displacements into trend,periodic and fluctuating components,and determine the representative environmental and load factors using hierarchical clustering of the principal components,so that these factors can be decomposed into the trend,low-and high-frequency components according to their physical meanings.Then,an optimized dynamic time warping algorithm based on a shape-based distance is used to divide the displacement components at the measured points into different deformation zones;for these zones,a sequence of their centroids is calculated to capture shared characteristics.The zonal data sets of the centroid sequences and their strongly related components of the dominant influencing factors can be established.Finally,we construct an improved zonal deformation prediction models using three machine learning algorithms-random forest,least squares support vector machine,and boosted regression tree-and an improved hydrostatic-thermal-time model.These improved models are verified against the measurements of Xiluodu super-high arch dam.The verification shows satisfactory results in accuracy and well explains the spatiotemporal correlation and differences in the trend,periodic and fluctuating components of dam displacements.