A multipoint prediction model for the deformation of concrete dams considering climatic features of high-altitude regions

Abstract

High-altitude regions are characterized by natural climatic features such as low temperatures and high radiation. Concrete dams built in these regions face more complex operating conditions. Traditional single-output deformation prediction models ignore the correlations between different monitoring points, making it difficult to assess the overall deformation behavior of concrete dams. The black-box nature of some machine learning models can lead to a lack of interpretability, affecting the practical application. Therefore, a multipoint prediction model (MPM) is proposed to analyze the deformation behavior of concrete dams in high-altitude regions. Firstly, the deformation monitoring points are divided based on the climatic features of high-altitude regions. Subsequently, the MPM is constructed using the multi-output CatBoost model and Quasi-Monte Carlo algorithm to achieve deformation prediction across different partitioned monitoring points. Finally, the elucidation of the MPM optimization procedure in a transparent manner alongside the quantification of the influence exerted by input variables on output outcomes notably augments the interpretive capacity of the MPM. The proposed model is validated using monitoring data from a concrete dam in a high-altitude region. The disparities in chaotic and entropy variation features among deformation monitoring points in different zones corroborate the appropriateness of the zoning in this study. Comparisons between the single-output CatBoost model and MPM verify the feasibility and efficiency of multipoint predictions. Comparisons with other multi-output prediction models validate the superior predictive performance of the MPM. Additionally, the proposed model's generalization performance is validated using data from another concrete arch dam in high-altitude regions.