Tags:Autoregressive integrated moving average, Dam monitoring, Exponential smoothing state space, Feed-forward neural network autoregression, Piezometer and Vector autoregression
Abstract:
The level of piezometric pressure at the concrete-rock interface and at discontinuities in the foundation of a concrete dam has a great influence on overturning movements. The objective of this study was the selection of models for the prediction of uplift pressures in the foundation of a concrete dam on the basis of historical time series values only. The models were automatically modeled using the fests package in the R software. The models were evaluated on the basis of RMSE. All of them satisfactorily represented the time series profile of the monthly averages of the measurements of six piezometers of the Itaipu Dam and were able to predict the long-term underpressures with reasonable accuracy. The results obtained indicate that the automation of the modeling can assist in the monitoring and follow-up of the evolution of the behavior of the dam in its current phase.
Selecting Time Series Models for Predicting Concrete Dam Foundation Uplift Pressure