scholarly journals Safety Monitoring of a Super-High Dam Using Optimal Kernel Partial Least Squares

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Hao Huang ◽  
Bo Chen ◽  
Chungao Liu
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Safety Monitoring ◽  
Deformation Monitoring ◽  
Abnormal Behavior ◽  
Dam Safety ◽  
Strongly Nonlinear ◽  
Kernel Partial Least Squares ◽  
Dam Safety Monitoring ◽  
Optimal Kernel

Considering the characteristics of complex nonlinear and multiple response variables of a super-high dam, kernel partial least squares (KPLS) method, as a strongly nonlinear multivariate analysis method, is introduced into the field of dam safety monitoring for the first time. A universal unified optimization algorithm is designed to select the key parameters of the KPLS method and obtain the optimal kernel partial least squares (OKPLS). Then, OKPLS is used to establish a strongly nonlinear multivariate safety monitoring model to identify the abnormal behavior of a super-high dam via model multivariate fusion diagnosis. An analysis of deformation monitoring data of a super-high arch dam was undertaken as a case study. Compared to the multiple linear regression (MLR), partial least squares (PLS), and KPLS models, the OKPLS model displayed the best fitting accuracy and forecast precision, and the model multivariate fusion diagnosis reduced the number of false alarms compared to the traditional univariate diagnosis. Thus, OKPLS is a promising method in the application of super-high dam safety monitoring.

scholarly journals Outlier Detection Based on Multivariable Panel Data and K-Means Clustering for Dam Deformation Monitoring Data

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jintao Song ◽  
Shengfei Zhang ◽  
Fei Tong ◽  
Jie Yang ◽  
Zhiquan Zeng ◽  
...  
Keyword(s):  
Panel Data ◽  
Outlier Detection ◽  
Correlation Coefficients ◽  
Safety Monitoring ◽  
Deformation Monitoring ◽  
Monitoring Data ◽  
Structural Safety ◽  
Dam Safety ◽  
Dam Safety Monitoring ◽  
Dam Deformation

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.

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