A novel Bayesian updating finite element model using enhanced unscented Kalman filter for time-dependent estimation of rockfill dam structure deformation

Abstract

Due to the time-dependent effect of rockfill dams, the conventional time-invariant finite element method (FEM) can hardly meet practical engineering requirements. This paper proposes an updating Bayesian FEM method for accurate long-term deformation analysis. A combined FEM model is introduced accounting for both instantaneous and creep behaviors. The FEM model is then updated using a Bayesian algorithm, unscented Kalman filter (UKF). The UKF calibrates the prior FEM predictions by incorporating real-time measurement data, thus iteratively reducing discrepancies between model predictions and actual observations. To further enhance the algorithm accuracy, a power-law-based fading memory factor is proposed to mitigate measurement noise in standard UKF. For parameter identification, a slice approach of the high-dimensional covariance confidence ellipsoid is developed. The methodology is validated in Qingyuan rockfill dam, in Guangdong province, China. Results show that the updated FEM is more consistent with the actual monitoring data. The fading memory improves standard UKF performance with a lower relative root-mean-square error (RRMSE). Additionally, the slice method reveals that a specific three-parameter configuration behaves better than the others. The proposed approach can also be extended to other fields including slope and tunneling.