Abstract
The seepage of a rockfill dam with a high core wall is an important and difficult issue in the safety monitoring of a core rockfill dam, something about which managers are immensely concerned. Seepage of a high core rockfill dam is mainly affected by factors such as water level, rainfall, temperature, filling height, and aging. The traditional research method is to establish a multiple linear regression model to analyze the influence factors of seepage. However, the multicollinearity between these factors affects parameter estimation, and random errors in the data cause the regression model to fail to be established. This paper starts with data collected by an osmometer, uses the 3δ criterion to process the outliers in the sample data, uses the R language to perform principal component analysis on the processed data to eliminate the multicollinearity of the factors, and finally uses multiple linear regression to model and analyze the data. Taking the Nuozhadu high core rockfill dam as an example, the influencing factors of seepage in the construction period and the impoundment period were studied and the seepage was then forecasted. This method provides guidance for further studies of the same type of dam seepage monitoring model.
Highlights
A core-wall rockfill dam is economical to invest in, simple to construct, and locally sourced
The Nuozhadu Hydropower Station is located at the lower reaches of the Lancang River at the junction of Cuiyun District and Jixian County, Simao City, Yunnan Province It is the fifth level of the eight cascade planning in the middle and lower reaches of the Lancang river
The monitoring of seepage is done by vibrating wire osmometer
Summary
A core-wall rockfill dam is economical to invest in, simple to construct, and locally sourced It has the advantages of good adaptability to dam foundation conditions, full use of construction excavation materials, and good seismic performance, and it plays an essential role in the development of water resources at home and abroad. The traditional research method has been to establish a multiple linear regression model to analyze the influence factor of seepage [10,11]. To solve the problems of the traditional methods, this paper first used the 3δ criterion to process the outliers in the data collected by the measurement and control unit and analyzed the principal component of the data without any random errors after processing, eliminating the multicollinearity between the factors [15].
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