The paper focuses on the failure process and mechanism of the concrete gravity dam considering different nonlinear models under strong earthquakes. By taking a typical monolith of a concrete gravity dam as a case study, a comparative analysis of the failure process and mechanism of the dam considering the plastic damage model and the dynamic contact model, respectively, is performed using the seismic overload method. Moreover, the ultimate seismic capacity of the dam is evaluated for both of the nonlinear models. It is found that the ultimate seismic capacity of the dam is slightly different, but the failure process has significant distinctions in each model. And, the damage model is recommended when the conditions permit.

The dynamic mechanical properties of dam concrete play a critical role in the seismic design of dams. Conducting experiments on large-scale fully graded specimens to accurately obtain the mechanical properties of dam concrete is a challenging task. Dam concrete performance is typically evaluated using standard-sized wet-screened specimens. Therefore, it is essential to understand the relationship between the mechanical properties of fully graded and wet-screened specimens under dynamic loading; however, research in this field is scarce. This study investigated the dynamic flexural and compression tests of fully graded and wet-screened dam concrete specimens under strain rates corresponding to seismic loading. The strain rates for the flexural tests were set to 10⁻⁶ s⁻¹, 2 × 10⁻⁴ s⁻¹, 8 × 10⁻⁴ s⁻¹, and 3 × 10⁻³ s⁻¹, while the strain rates for the compression tests were set to 10⁻⁶ s⁻¹, 5 × 10⁻⁴ s⁻¹, 8 × 10⁻³ s⁻¹, and 2 × 10⁻² s⁻¹. Failure modes and strain–stress curves were obtained for various strain rates. Based on the experimental data, the strength, elastic modulus, crack propagation velocity, cracking stress, cracking strain, peak compressive strain, and compressive energy absorption capacity of dam concrete under dynamic strain rates corresponding to seismic loading were determined. The effects of strain rate on these mechanical properties of dam concrete were analyzed. The relationship between fully graded and wet-screened concrete was explored, leading to a linear empirical formula for predicting the strength and elastic modulus of dam concrete based on wet-screened concrete test data. This study enriches the experimental research on the mechanical properties of dam concrete, deepens the understanding of the relationship between fully graded and wet-screened specimens, and provides scientific experimental data to support the seismic safety design of dams.

The seismic safety evaluation of the dam is performed based on either dam strength failure or dam abutment instability failure according to the traditional deterministic methods and concepts in the current code. However, these two failure modes are interactive and inseparable, considering only one failure mode under a strong earthquake fails to fully reflect the actual seismic performance of high arch dams. To develop a more realistic seismic safety evaluation framework for high arch dams, this paper constructs a finite element analysis model that considers the coupling of dam strength failure and dam abutment instability failure. The nonlinear dynamic response analysis of the high arch dam-foundation coupling system is conducted using the seismic overload analysis method. Different performance evaluation indexes, such as damage depth-thickness ratio, sliding area ratio, and the residual displacement of the dam crest relative to the dam bottom in the stream direction, are proposed. The performance evaluation criteria and ultimate seismic capacity are defined and quantitatively assessed. The results indicate that 2.0 times the horizontal PGA can be considered the ultimate seismic capacity of the high arch dam-foundation coupling system, providing a reliable scientific basis for seismic safety evaluation of high 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.