A spatio-temporal dam deformation zoning method considering non-uniform distribution of monitoring information
A spatio-temporal dam deformation zoning method considering non-uniform distribution of monitoring information
复制标题
考虑监测信息分布不均匀的大坝变形时空分区方法
DOI：
10.1109/access.2021.3106817
复制
发表时间：
2021
期刊：
影响因子：
3.9
通讯作者：
Zikang Xing
中科院分区：
文献类型：
--
作者：
Jiayi Wang;Hao Gu;Bo Chen;Chongshi Gu;Qinuo Zhang;Zikang Xing
变形是混凝土坝实际工作状态最直观的指标。大坝变形变化规律分区是大坝安全评价和风险评价的关键内容之一。然而，反映变形和变化特征信息的样本点分布不均匀，难以用传统的聚类方法对数据样本进行聚类。针对这一问题，提出了一种考虑监测信息非均匀分布的大坝变形时空分区方法。该方法首先利用经过预处理的变形数据，分别以绝对变形量、变形量增量和相对变形量增量建立相似-距离分区指标，然后将变形数据转换到笛卡尔坐标系中，称为样本点。其次，利用改进的基于密度的噪声应用空间聚类(DBSCAN)算法对点进行聚类。聚类参数<；inline-FORMULE>；<；tex-ath notation=“LaTeX”>；$M$<；/Tex-ath&>；<；/inline-FORMULE>；和<；INLINE-FORMUMAL>；<；tex-ath notation=“LaTeX”>；$\Delta$<；/Tex-ma&>；<；/INLINE-FORMULE>；由以评价指标为目标函数的优化算法确定，然后通过动态更新邻域半径值<；内联公式>；<；tex-ath notation=“LaTeX”>；$\varepsilon$<；/tex-ath>；/内联公式>；对代表时间段或空间监控点的样本点进行聚类。此外，通过选取几个人工数据集，验证了改进的DBSCAN算法在非均匀聚类方面比传统算法具有更明显的优势。给出了一座既有混凝土坝的变形数据，并对其进行了讨论，以验证所建立的分区方法。
Deformation is the most intuitive indicator of the actual working status of a concrete dam. Zoning the variation regulation of dam deformation is one of the key parts of dam safety evaluation and risk assessment. However, the sample points reflecting deformation and variation characteristic information are non-uniformly distributed, thus it is difficult to cluster the data samples by traditional clustering methods. To solve this problem, a spatio-temporal zoning method of dam deformation considering non-uniform distribution of monitoring information is proposed. Firstly, the preprocessed deformation data are utilized to establish the similarity-distance zoning indicators using the absolute deformation, the deformation increase and the relative deformation increase respectively; then the deformation data are transferred into the Cartesian coordinate system, known as sample points. Secondly, utilize the improved Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to cluster the points. The clustering parameters <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> are determined by an optimization algorithm with an evaluation index as the objective function, then the sample points representing time sections or spatial monitoring points are clustered through dynamically updating the neighborhood radius value <inline-formula> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula>. Moreover, several artificial data sets are selected to demonstrate that the improved DBSCAN algorithm is with more obvious superiority in non-uniform clustering compared to traditional algorithms. Deformation data of an existing concrete dam are presented and discussed to validate the established zoning method.