Machine Learning-Based Rapid Seismic Damage Evaluation for Concrete Arch Dams

A fast and accurate assessment of earthquake-induced damage in arch dams is critical for emergency management, yet conventional approaches, such as field investigations, numerical simulations, and fragility-based assessments, are inadequate for timely and reliable damage evaluation. This study proposes a rapid seismic damage state assessment framework for arch dams based on machine learning techniques. Feature screening, machine learning algorithm selection, and hyperparameter optimization are systematically incorporated to develop prediction models with strong generalization capability. The proposed methodology is demonstrated through a case study of a high arch dam in southwestern China. A total of 1000 dynamic simulations are performed to generate a ground motion intensity measure-seismic damage state dataset for model training and testing. By considering five feature scenarios and employing six machine learning algorithms, 30 candidate machine learning models are developed and evaluated. Among all the developed prediction models, the ANN model under the 20-feature scenario achieves the best overall predictive performance, with an overall accuracy of 0.88. The trained model can be deployed in earthquake alert systems to rapidly evaluate the seismic damage state of arch dams using monitored ground motion data, thereby facilitating emergency decision-making.