Data-driven iterative calibration method for prior knowledge of earth-rockfill dam wetting model parameters

Wetting deformation in earth-rockfill dams is a critical factor influencing dam safety. Although numerous mathematical models have been developed to describe this phenomenon, most of them rely on empirical formulations and lack prior knowledge of model parameters, which is essential for Bayesian parameter inversion to enhance accuracy and reduce uncertainty. This study introduces a data-driven approach to establishing prior knowledge of earth-rockfill dams. Driving factors are utilized to determine the potential range of model parameters, and settlement changes within this range are calculated. The results are iteratively compared with actual monitoring data until the calculated range encompasses the observed data, thereby providing prior knowledge of the model parameters. The proposed method is applied to the right-bank earth-rockfill dam of Danjiangkou. Employing a Gibbs sample size of 30,000, the proposed method effectively calibrates the prior knowledge of the wetting model parameters, achieving a root mean square error (RMSE) of 5.18 mm for the settlement predictions. By comparison, the use of non-informative priors with sample sizes of 30,000 and 50,000 results in significantly larger RMSE values of 11.97 mm and 16.07 mm, respectively. Furthermore, the computational efficiency of the proposed method is demonstrated by an inversion computation time of 902 s for 30,000 samples, which is notably shorter than the 1026 s and 1558 s required for non-informative priors with 30,000 and 50,000 samples, respectively. These findings underscore the superior performance of the proposed approach in terms of both prediction accuracy and computational efficiency. These results demonstrate that the proposed method not only improves the predictive accuracy but also enhances the computational efficiency, enabling optimal parameter identification with reduced computational effort. This approach provides a robust and efficient framework for advancing dam safety assessments.