Research on Temperature Decomposition and Its Influence on Deformation of Rockfill Dams Based on Intelligent Algorithms
Abstract
The unclear impact of temperature on rockfill dam settlement and the lack of a solid basis for selecting temperature parameters in prediction models are problematic. These issues significantly limit the accuracy and applicability of deformation monitoring models for rockfill dams. For this reason, a method for decomposing the settlement components of rockfill dams, along with an intelligent prediction approach, is proposed. The Bayesian optimization (BO) algorithm is employed to optimize the hyperparameters of the Bayesian dynamic linear model (BDLM), enabling a comprehensive exploration of the correlation between rockfill dam settlement and temperature factors. Based on this, a BO–BDLM‐based decomposition model is constructed to quantify the contribution of the temperature factor to settlement behavior. Spatiotemporal analysis is conducted to uncover the evolution patterns of various influencing components, revealing the underlying mechanism by which temperature affects settlement. Furthermore, both a full‐feature model and a simplified prediction model are developed to predict settlement, and their prediction accuracies are compared. The contribution of the temperature factor is quantitatively assessed using the SHapley Additive exPlanations (SHAP) method. Example analyses demonstrate that our BO–BDLM significantly improves performance and accurately isolates the temperature factor consistent with rockfill dam deformation characteristics. The temperature component contributes approximately 2%–4% of total settlement but accounts for 38.39% of model importance. This pivotal factor substantially enhances prediction accuracy. By quantitatively assessing temperature influence and establishing its selection basis, our study offers valuable insights for the safety evaluation of rockfill dams and related engineering projects.