Deformation prediction based on denoising techniques and ensemble learning algorithms for concrete dams

Abstract

Constructing a deformation prediction model for dams that can accurately capture deformation trends is crucial to ensure their operational safety. The accurate monitoring data are critical to the presentation of the variation patterns of the monitored quantities. However, due to various disturbances, there can be a lot of noise in the raw deformation monitoring data acquired by the sensors. Therefore, the reduction of the noise level in the data sequence is necessary for the improvement of the accuracy and reliability of the prediction model. This paper presents data denoising techniques for the removal of outliers from data sequences, including complete ensemble empirical mode decomposition with adaptive noise, garrote threshold functions, and sample entropy. The ensemble learning algorithm was then employed to complete the deformation prediction of concrete dams. Through example analysis and comparison with a benchmark model, the proposed model's performance was validated, highlighting its effectiveness and superiority. Additionally, this paper enhances the model's interpretability by visualizing the optimization process. The degree of influence of the input factors on the deformation prediction results is also analyzed using the feature importance metric. The proposed model is expected to be applied to deformation monitoring of other civil and hydraulic structures, as it can effectively reduce the impact of noise on prediction.