Dynamic modelling of displacements on an embankment dam using the Kalman filter

Abstract

For embankment dams, the modelling of influences in the dam response becomes more difficult if the empirical model cannot be used due to changing external influences. In this work, an attempt at modeling the measured deformations as a dynamic stochastic process is presented. A discrete Wiener process acceleration model (DWPAM), which is based on Kalman filtering, is implemented on geodetically measured displacements of the point on a rock-fill embankment dam. The acceleration is modelled as a zero-mean white sequence. The verification of a filter design and choosing an appropriate value of the process noise intensity scalar is controlled primarily with the compliance of the statistical tests in the domain of measurements and in the system state domain. In the case study, it was shown that the DWPAM can detect statistically significant changes in measured displacements according to the previous behaviour and can be used for the identification of potential anomalies.