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A novel model of dam displacement based on panel data
Author(s) -
Shao Chenfei,
Gu Chongshi,
Yang Meng,
Xu Yanxin,
Su Huaizhi
Publication year - 2018
Publication title -
structural control and health monitoring
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.587
H-Index - 62
eISSN - 1545-2263
pISSN - 1545-2255
DOI - 10.1002/stc.2037
Subject(s) - multicollinearity , statistic , safety monitoring , deformation monitoring , displacement (psychology) , estimator , regression analysis , engineering , data mining , statistics , deformation (meteorology) , computer science , mathematics , geology , psychology , oceanography , microbiology and biotechnology , psychotherapist , biology
Deformation monitoring is the main program in the area of dam safety. Because statistical model is simple and intuitive, it is widely used in dam safety monitoring. However, in dam's displacement statistic model, there is a high degree of linear relationship between influence factors. Due to the influence of multicollinearity, models calculated with traditional methods are not accurate and stable. Besides, because of dam integrity, each part of dam is interrelated and interactive. Currently, single point or multipoints displacement monitoring models cannot accurately reflect the actual dam running state. In this paper, the theory of panel data is introduced to dam deformation analysis. Panel data contain time series data and cross section data, which is able to solve serious multicollinearity problem of traditional regression method. Moreover, all measuring points are classified into several groups according to their similar deformation law. Based on the random‐coefficient model of panel data, potential relationship between different measuring points is built. Take 1 hydropower station, for example, to examine that random‐coefficient model is able to improve the modeling situation that estimators are not significant and simultaneously provide a stable model, which explores a new approach for the research of dam displacement monitoring.