基于多测点云相似的混凝土坝变形性态关联分析

Correlation analysis of deformation behavior of concrete dams based on similarity of multi-point cloud

大型混凝土坝的安全监测测点数量多、布置广泛,海量监测数据给及时准确分析评估大坝安全性态带来困难,为此引入云模型理论对变形监测资料表征混凝土坝性态的差异性及关联性进行分析。将大坝每个测点的监测数据视为一个云,单个测值视为云滴,进行云参数计算获取测值序列的云数字特征,进而通过多测点的云参数,计算测点之间的相似度和相似系数,用以表征不同测点反映大坝整体变形状态的差异和相关性。实例分析表明,云相似度能够迅速发现同类坝段中的异常测点,通过云相似系数的聚类分析实现测点分组,在实现大坝变形性态关联分析的同时,测点分组管理可有效提高监测数据分析效率。

The number of safety monitoring points of large-scale concrete dams is large and widely arranged.The analysis of the massive monitoring data makes it difficult to assess the safety state of dams timely and accurately,so the cloud model theory is introduced to analyze the difference and correlation of deformation monitoring data characterizing concrete dams.The monitoring data of each monitoring point of the dam is regarded as a cloud,and the monitoring values are regarded as cloud drops.The cloud parameters are then calculated to obtain the cloud numerical characteristics of the monitoring value sequence.Case analysis shows that the abnormal monitoring points in similar dam sections can be quickly discovered by the cloud similarity,and the clustering analysis of cloud similarity coefficients can be used to group the monitoring points.The grouping management of monitoring points can effectively improve the analysis efficiency of the monitoring data while realizing the correlation analysis of dam deformation properties.