Abstract
External deformation monitoring of high core rock-fill dams (HCRFDs) is an important and difficult part of safety monitoring. The traditional method of external deformation monitoring and data analysis for HCRFDs is to use a total station for small angle observations and establish a regression model to analyze the results. However, the small angle method has low accuracy and a low automation degree, and there is multicollinearity between the independent variables, which affects the parameter estimation and leads to the failure of model establishment. The angle forward intersection method is adopted in this paper for observation, and an improved partial least squares method (IPLS) is proposed to eliminate the multicollinearity of the independent variables. Compared to the traditional method, the improved observation method exhibits high accuracy and a high automation degree. The new data analysis method can not only eliminate multicollinearity but also improve the interpretation ability of the model. The data from the initial stage of water storage shows that the displacement increases with the increase in the upstream water level and time, and the speed of water storage is proportional to the displacement. The water level and time are the main influencing factors. This conclusion provides a theoretical basis for reservoir management departments to control water levels and gate opening and closing. The method in this paper can be applied to arch dams, gravity dams, and other types of waterpower engineering systems.
Highlights
High core rock-fill dams (HCRFDs) are inexpensive and simple to construct and can be constructed from locally sourced materials
Aiming at the issues of the external deformation monitoring methods and data analysis of traditional high core rock-fill dams (HCRFDs), an angle forward intersection method using a total station and an improved partial least squares (IPLS) data analysis method is presented in this paper
To address the failure of the principal component analysis (PCA) and PLS regression, we propose an improved partial least squares (IPLS) method, based on an orthogonal projection, which eliminates the information that is irrelevant to the dependent variables
Summary
High core rock-fill dams (HCRFDs) are inexpensive and simple to construct and can be constructed from locally sourced materials. Some studies use a partial least squares regression (PLSR) method for the dam displacement monitoring and they use the PLS to establish the model directly [21], while not considering the situation of the extracted components containing significant amounts of information that is unrelated to the dependent variables. Aiming at the issues of the external deformation monitoring methods and data analysis of traditional HCRFDs, an angle forward intersection method using a total station and an improved partial least squares (IPLS) data analysis method is presented in this paper. Taking the measured initial storage data of the Nuozhadu HCRFD as an example, the practicability of the new observation and data analysis methods are studied to explore the relationship between the HCRFD downstream rock-fill displacement and the independent variable factors during initial storage This conclusion provides a theoretical basis for reservoir management departments to control water levels and gate opening and closing.
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