Use of machine learning in predicting structural performance of dams

Dams are vital civil infrastructure for water supply, hydropower generation, and flood control, but their structural integrity is continuously challenged by aging, environmental loads, and climate variability. Traditional structural analysis techniques often fall short in real-time risk assessment and anomaly detection due to their deterministic nature and data limitations. Machine learning (ML), with its ability to learn patterns from historical and real-time sensor data, is increasingly being employed to predict the structural performance of dams. This paper reviews recent advancements in applying ML models for dam monitoring and performance prediction, discusses commonly used algorithms, evaluates data sources and preprocessing techniques, and outlines practical applications such as crack detection, seepage estimation, deformation modeling, and failure forecasting. The study also highlights the challenges related to data quality, model interpretability, and real-world deployment, while suggesting future directions for integrating ML with digital twin technologies and decision support systems in dam safety engineering.