Temperature is one of the main loads of super-high arch dams. (1) Background: a super-high arch dam in southwest China was taken as an example in this paper and the temporal-spatial evolution law of dam temperature was analyzed based on the monitoring data. (2) Methods: the finite element simulation analysis method was adopted to invert the boundary conditions of temperature on the upstream surface and the thermal parameters of the concrete, and the temperature evolution process of the arch dam in long-term operation was simulated and analyzed. After the distribution characteristics of the designed reservoir water temperature and the actual reservoir water temperature were compared, the difference in the temperature field of the arch dam during the impoundment and operation under the designed and actual conditions was studied. (3) Results: the temporal-spatial evolution law of the temperature in the dam operation period accords with the conventional knowledge, and the calculated value through simulation feedback is in good agreement with the monitoring value, which can reflect the actual temperature field distribution of the dam. (4) Conclusions: under the design condition, the dam temperature rose slowly after closure grouting and then tended to be stable. Under the actual condition, the temperature rose by 7.1~9.2 °C after closure grouting, reached the highest temperature in about 8~12 years, and fell back to a stable temperature in 40~80 years.

In response to the challenge of limited model availability for predicting the lifespan of super-high arch dams, a hybrid model named EMD-PSO-GPR (EPR) is proposed in this study. The EPR model leverages Empirical Mode Decomposition (EMD), Gaussian Process Regression (GPR), and Particle Swarm Optimization (PSO) to provide an effective solution for super-high arch dam stress prediction. This research focuses on three strategically selected measurement points within the dam, characterized by complex stress conditions. The predicted results from the EPR are compared with those from GPR, Long Short-Term Memory (LSTM), and Support Vector Regression (SVR), using actual stress data measured at research points within a super-high arch dam in Southwest China. The findings reveal that the proposed EPR model attains a maximum mean absolute error (MAE) of 0.02916 and a maximum root mean square error (RMSE) of 0.03055, surpassing the compared models. As a result, the EPR model introduces an innovative computational framework for stress prediction in super-high arch dams, excelling in handling stress data characterized by high vibration frequencies and providing more accurate predictions.

Difficulty in clarifying the deformation characteristics of deep rocks under a high water pressure environment is a technical bottleneck restricting the safe operation of large hydropower stations. In order to study the effect of reservoir water level changes on the mechanical behavior of deep limestone, a series of mechanical tests were conducted under different dynamic high water pressure environments using a self-developed hydraulic loading test device. The test results show that the unsaturated limestone always undergoes compressive deformation during the linear increase in external water pressure, and the saturated limestone changes its deformation state from compression to expansion during the linear decrease in external water pressure. The stress–strain curve of limestone shows apparent hysteresis characteristics during the cyclic increase and decrease in external water pressure. Overall, the rock strain rate showed a significant negative correlation with the external water pressure, and the rock deformation modulus showed a certain positive correlation with the external water pressure. During hydraulic loading, saturated rocks had a smaller range of variation in the strain rate and deformation modulus and were more resistant to deformation than unsaturated rocks. Limestone was subjected to both external water pressure and internal pore water pressure in a cyclic cycle, where pore water pressure promotes pore creation and expansion, while external water pressure prevents water from degrading the pore structure. The periodic change of water pressure has a significant influence on rock mechanics and deformation behavior, and the rock mass will undergo elastic deformation, plastic deformation, and even fracture. Further study of this deformation rule can provide a more accurate theoretical basis for the safe operation of water conservancy projects.

Cascading failures of clustered landslide dams can intensify downstream hazards not only by increasing peak flood magnitude but also by accelerating the rise of water level immediately upstream of successive dams, thereby shortening the available response time before overtopping. This study reports large-scale flume experiments on a three-dam cascade built with identical geometry and similar soil gradation, while systematically varying longitudinal spacing and inflow discharge. The principal measured variable, $C_w\left(t\right)$, is defined here as the local forebay run-up/water-level record measured at a fixed gauge position immediately upstream of each dam. The run-up hydrographs were summarized using peak run-up $C_w^{\mathrm{m}\mathrm{a}\mathrm{x}}$, threshold-arrival time $t_a$ defined at $0.1 C_w^{\mathrm{m}\mathrm{a}\mathrm{x}}$, time to peak $t_p$, maximum rising-stage rate $S^{\mathrm{m}\mathrm{a}\mathrm{x}}$, and above-threshold duration $T$. Across ten tests (five spacing configurations under low/high discharge), peak run-up at both downstream dams consistently exceeded that at Dam1, with amplification factors relative to Dam1 of 1.11–1.45 at Dam2 and 1.13–1.42 at Dam3; Dam3 was not always higher than Dam2. Amplification was much stronger in the rising-stage dynamics: $S^{\mathrm{m}\mathrm{a}\mathrm{x}}$ increased relative to Dam1 by factors of 1.56–11.0 at Dam2 and 2.27–14.0 at Dam3, demonstrating pronounced downstream wavefront steepening. Higher discharge produced earlier threshold arrivals and peaks throughout the cascade, whereas shorter spacing generally produced more impulsive downstream responses with sharper peaks and larger rate amplification. Overall, the dataset provides stage/run-up-based constraints on cascade amplification and indicates that, within the present experimental matrix, dam spacing is the dominant geometric control on flood propagation and downstream hazard escalation.

Crack defects pose a significant threat to the safe operation of the foundational structures of hydropower stations. Therefore, crack detection in a plunge pool corridor is crucial for the safe operation and maintenance of hydropower hubs. Addressing challenges such as missing crack datasets, complex and diverse crack shapes, and difficulties in feature extraction, this paper proposes an improved U-Net model. The model integrates the CDIAM attention module within the U-Net architecture and is optimized through the incorporation of the MIDConv convolutional block. The enhanced U-Net model demonstrates outstanding performance on a self-constructed plunge pool corridor crack dataset, achieving an MIoU, F1-score, MPA, and accuracy of 84.44%, 93.05%, 89.75%, and 99.46%, respectively. Compared to the original U-Net model, these metrics show improvements of 5.34%, 4.90%, 3.39%, and 0.36%, respectively. The improved U-Net model exhibits higher detection accuracy in corridor crack segmentation tasks, effectively extracting feature information from irregular shapes, and provides a superior solution for crack defect detection in a plunge pool corridor, thereby enhancing the safety and efficient maintenance of hydropower stations.