Abstract
A dam is a super‐structure widely used in water conservancy engineering fields, and its long‐term safety is a focus of social concern. Deformation is a crucial evaluation index and comprehensive reflection of the structural state of dams, and thus there are many research papers on dam deformation data analysis. However, the accuracy of deformation data is the premise of dam safety monitoring analysis, and original deformation data may have some outliers caused by manual errors or instruments aging after long‐time running. These abnormal data have a negative impact on the evaluation of dam structural safety. In this study, an analytical method for detecting outliers of dam deformation data was established based on multivariable panel data and K‐means clustering theory. First, we arranged the original spatiotemporal monitoring data into the multivariable panel data format. Second, the correlation coefficients between the deformation signals of different measuring points were studied based on K‐means clustering theory. Third, the outlier detection rules were established through the changes of the correlation coefficients. Finally, the proposed model was applied to the Jinping‐I Arch Dam in China which is the highest dam in the world, and results indicate that the detection method has high accuracy detection ability, which is valuable in dam safety monitoring applications.
Highlights
Since the 19th century, there have been many dam-break events in the world, such as Malpasset Dam (France, 1959), Vajont Dam (Italy, 1963), and Banqiao Dam (China, 1975), which brought heavy disasters and huge economic losses to the relevant countries [1, 2]
Among the various monitoring subjects, deformation is an comprehensive reflection of dam safety behaviors which can be effectively assessed through the analysis of dam deformation data [4]
A large number of dam safety analysis studies have been carried out based on the original deformation monitoring data
Summary
Since the 19th century, there have been many dam-break events in the world, such as Malpasset Dam (France, 1959), Vajont Dam (Italy, 1963), and Banqiao Dam (China, 1975), which brought heavy disasters and huge economic losses to the relevant countries [1, 2]. Among the various monitoring subjects, deformation is an comprehensive reflection of dam safety behaviors which can be effectively assessed through the analysis of dam deformation data [4]. After the 20th century, with the gradual development of artificial intelligence, artificial intelligence algorithm is used to simulate the input-output relationship of dam deformation, and many high-precision analysis models are established [6,7,8]. A large number of dam safety analysis studies have been carried out based on the original deformation monitoring data. The accuracy of original deformation monitoring data is the foundation of dam safety analysis. Dam deformation data are mainly obtained through automatic system acquisition or manual reading, which may have some outliers due to the monitoring instrument aging, artificial error, structural state change, etc [9]. Deformation outlier usually deviates from the normal value, which affects the correctness of dam safety evaluation. erefore, detecting the outliers of deformation data should be conducted before
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