Improve the Model Stability of Dam's Displacement Prediction Using a Numerical-Statistical Combined Model

Abstract

In most studies of dam's displacement prediction based on monitoring data, emphasis was given on improving the prediction accuracy, while the model stability was merely considered. This study proposed a numerical-statistical combined model which aims to improve the model stability. The displacement was modelled within three modules: recoverable displacement (i.e., displacement induced by the external load including the water pressure and temperature), non-recoverable displacement (i.e., displacement due to the inherent variations of the materials such as the creep and fatigue of the concrete), and measurement errors (i.e., instrument error and human error). To reduce the random errors and increase the model stability, we used the numerical simulation to constrain the coefficients of explanatory variables for the recoverable displacement. The non-recoverable displacement was estimated by empirical equations, and the measurement errors were given by Gaussian distributions. The randomness of coefficients in the model among all monitoring points are constrained further by random coefficient model. We adopted the root mean square error (RMSE) at varying time and the change ratio of the coefficients (CRC) to evaluate the model stability. Results indicated that the proposed model not only has better prediction accuracy but also has better model stability compared with the statistical model and coordinates-included statistical model proposed in previous studies.