Abstract
This study determines dam deformation similarity indexes based on an analysis of deformation zoning features and panel data clustering theory, with comprehensive consideration to the actual deformation law of super-high arch dams and the spatial–temporal features of dam deformation. Measurement methods of these indexes are studied. Based on the established deformation similarity criteria, the principle used to determine the number of dam deformation zones is constructed through entropy weight method. This study proposes the deformation zoning method for super-high arch dams and the implementation steps, analyzes the effect of special influencing factors of different dam zones on the deformation, introduces dummy variables that represent the special effect of dam deformation, and establishes a variable-intercept panel model for deformation zoning of super-high arch dams. Based on different patterns of the special effect in the variable-intercept panel model, two panel analysis models were established to monitor fixed and random effects of dam deformation. Hausman test method of model selection and model effectiveness assessment method are discussed. Finally, the effectiveness of established models is verified through a case study.
Highlights
The entire service period of a super-high arch dam can be divided into several stages, and each stage presents a different deformation behavior pattern
Analysis models are based on one-dimensional time series of single measuring point
Because common influencing factors cannot depict the different deformation laws of different regions, this study introduced dummy variables that can represent the effect of special influencing factors of different regions, called special deformation effect variables
Summary
The entire service period of a super-high arch dam can be divided into several stages, and each stage presents a different deformation behavior pattern. Determining the number of dam deformation zones and zoning process To address the second key problem, this study assumes that N measuring points of the dam are divided into k regions () based on Ward clustering and combined with the proposed similarity measurement G1, G2, ..., Gk. Let Nl represent the number of Gl measuring points, Xl be the mean measured value of Gl measuring points, and Xil be the deformation of the ith measuring point (i = 1, 2, ..., Nl) in Gl. For the deformation data of N measuring points during T, the sum of squares of deviations of sequence at different measuring points in Gl is
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