Summary
In order to discover anomalies of dam structure behaviors and evaluate the operation status timely, it is quite demanding to analyze the dam safety monitoring data that has been collected from the instruments. However, outliers in original monitoring data may affect the accuracy of dam performance assessment, which need to be detected before analyzing monitoring data. Model‐based methods have been applied in outlier detection as a kind of common method for a long time, but they generally rely heavily on model accuracy and easily lead to misjudgment of outliers once the data structure is complex. Considering the monitoring data of dam effect variables (e.g., deformation, cracking, or seepage) tend to show strong continuity, complex periodic and trending changes with the environment, valid monitoring data can reflect the variation trend by forming a data process line. Therefore, data that deviate from the process line can be detected as outliers. In this paper, an automatic process line identification method for dam safety monitoring data outlier detection is proposed. First, after drawing a scatter plot of the dam monitoring data, a binary image of the scatter plot is inputted into the computer program. Afterwards, the binary image would be processed by Gaussian blur and image binarization techniques, and then the continuous points could be identified. After constant adjustment of the vertical ordinate range and introducing Cuckoo Search (CS) algorithm, the optimal process line identification and outlier detection were finally completed. The case studies demonstrate the proposed method can enhance the efficiency of outlier detection.
During the operational period, unexpected upstream deformation has been observed in several super-high arch dams located in the alpine and gorge regions. In addition, the phenomenon of the downstream dam deformation monitoring values being apparently smaller than the numerical simulation results appears in some super-high arch dams. This paper focuses on the genetic mechanism of a super-high arch dam’s special deformation characteristics. The finite element method (FEM) was used to analyze the effects of solar radiation, valley contraction, and overhanging on super-high arch dam’s deformation behavior. First, the influences of solar radiation on the temperature field and deformation characteristics of the super-high arch dam under the shading effects of the mountain and the dam body were investigated. Second, the impacts of valley contraction on the deformation characteristics of the super-high arch dam during the storage period were studied. Subsequently, the impact of the overhanging effect on the super-high arch dam’s deformation was explored. Finally, a case study was conducted on the basis of the Jinping I super-high arch dam to evaluate the effectiveness of the proposed analytical method. It is indicated that the dam’s special deformation can be explained reasonably. Above all, in order to accurately analyze and predict the deformation characteristics of super high-arch dams in the alpine and gorge regions of Southwest China, solar radiation, valley contraction, and the dam-overhanging effect need to be considered as influencing factors of dam deformation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.