Predictive modeling of concrete arch dam behavior: evaluating the efficacy of Random Forest and Radial Basis Function Networks

Abstract

This study investigates the application of established open-source machine learning tools, specifically CatBoost, XGBoost, LightGBM, and TensorFlow, which are based on Forest and Radial Basis Function Networks, to predict and analyze the structural behavior of concrete arch dams. Utilizing the Karun-I dam as a case study, the research assesses the performance of various machine learning frameworks. The results demonstrate that Random Forest-based methods achieve superior prediction accuracy and computational efficiency in comparison to Radial Basis Function Networks. Additionally, the analysis emphasizes the critical influence of lake levels as the primary factor impacting dam displacement, as revealed through feature importance evaluation. Overall, this research underscores the promising potential of machine learning in enhancing structural health monitoring for large dams, offering significant insights that contribute to the improvement of safety measures and operational efficiency in dam management.