Accurate and reliable prediction of dam deformation (DD) is of great significance to the safe and stable operation of dams. In order to deal with the fluctuation characteristics in DD for more accurate prediction results, a new hybrid model based on a decomposition-ensemble model named VMD-SE-ER-PACF-ELM is proposed. First, the time series data are decomposed into subsequences with different frequencies and an error sequence (ER) by variational mode decomposition (VMD), and then the secondary decomposition method is introduced into the prediction of ER. In these two decomposition processes, the sample entropy (SE) method is innovatively utilized to determine the decomposition modulus. Then, the input variables of the subsequences are selected by partial autocorrelation analysis (PACF). Finally, the parameter-optimization-based extreme learning machine (ELM) models are used to predict the subsequences, and the outputs are reconstructed to obtain the final prediction results. The case analysis shows that the VMD-SE-ER-PACF-ELM model has strong prediction ability for DD. The model is then compared with other nonlinear and time series models, and its performance under different prediction periods is also analyzed. The results show that the proposed model is able to adequately describe the original DD. It performs well in both training and testing stages. It is a preferred data-driven model for DD prediction and can provide a priori knowledge for health monitoring of dams.

Magnetorheological (MR) dampers, as an intelligent vibration damping device, can quickly change the damping size of the material in milliseconds. The traditional semiactive control strategy cannot give full play to the ability of the MR dampers to consume energy and reduce vibration under different currents, and it is difficult to control the MR dampers accurately. In this paper, a semiactive control strategy based on reinforcement learning (RL) is proposed, which is based on “exploring” to learn the optimal value of the MR dampers at each step of the operation, the applied current value. During damping control, the learned optimal action value for each step is input into the MR dampers so that they provide the optimal damping force to the structure. Applying this strategy to a two‐layer frame structure was found to provide more accurate control of the MR dampers, significantly improving the damping effect of the MR dampers.

Affected by various complex factors, dam deformation monitoring data usually reflect volatility and non-linear characteristics, and traditional prediction models are difficult to accurately capture the complex laws of dam deformation. A multi-scale deformation prediction model based on Variational Modal Decomposition (VMD) signal decomposition technology is proposed in this study. The method first decomposes the original deformation sequence into a series of sub-sequences with different frequencies, then the decomposed sub-sequences are modeled and predicted by Long Short-Term Memory neural network (LSTM) and Random Forest (RF) according to different frequencies. Finally, the prediction results of all sub-sequences are reconstructed to obtain the final deformation prediction results. In this process, it is proposed to use the instantaneous frequency mean method to determine the decomposition modulus of VMD. The innovation of this paper is to decompose the monitoring data with high volatility, and use LSTM and RF prediction, respectively, according to the frequency of the monitoring data, so as to realize the more accurate capture of volatility data during the prediction process. The case analysis results show that the proposed model can effectively solve the negative impact of the original data volatility on the prediction results, and is superior to the traditional prediction models in terms of stability and generalization ability, which has an important reference value for accurately predicting dam deformation and has far-reaching engineering significance.

The traditional Three‐dimensional (3D) model for stability analysis of a large underground cavern group only includes the main powerhouse, the main transformer chamber, the tailrace surge tank, the main electrical wire hall, and part of the diversion tunnel and tailwater tunnel. Other caverns excavation are not taken into consideration, which is inconsistent with the actual project. In this study, a 3D Finite Element Method (FEM) model, including all the cavities, and a 3D simplified model containing only part of the cavities were established based on the actual underground cavern group project. We study the structural characteristics of the cavern group during excavation via 3D nonlinear FEM. At the same time, three numerical calculation schemes are designed for comparison. The results show that the simulated distributions of the stress field, deformation field, and plastic zone of the two numerical models are similar, but the magnitudes of the 3D FEM model, which considers all caverns are generally higher than the simplified model. The difference between the two simulated horizontal displacements of the main powerhouse reaches 53.72%. Therefore, from the perspective of engineering safety and actual construction, it is advisable to adopt the 3D FEM model, including all the cavities, when conducting a simulation analysis of the underground cavern group.

With the continuous development of hydropower stations, the capacities and the heads of hydro generator units are increasing, and the plant vibration problem is becoming more and more serious. A numerical simulation method for the vibration reduction control of magnetorheological (MR) dampers suitable for large-scale complex structures was proposed. The method is simple and easy to implement, and the semiactive control of the MR damper could be achieved by adjusting the current switch and size. On the basis of a numerical simulation, a mathematical model for the optimal layout of an MR damper device was established. The objective function was the vertical velocity and the vertical acceleration response of the generator floor. The results showed that the proposed semiactive control numerical simulation method could be applied to the vibration control of the hydropower plant structure, and the vertical velocity and vertical acceleration were reduced by 10.96% and 12.90%, respectively, compared with those without structural vibration control. At the same time, the proposed optimized layout method was effective and feasible, and the damping effect of the MR damper could be effectively improved through the optimized layout.

The computational efficiency and nonconvergence of the iteration are two main difficulties in contact problems, especially in the creep of the foundation. This paper proposes a method to analyze the structural soft foundation system affected by time. The methodology is an explicit method, combining the finite element method with the analytical method. The creep deformation of soft foundation is obtained based on Laplace transforms. The structural deformation contains the statically determinate structural deformation and rigid body displacement, solved by the finite method. The contact forces are calculated by the deformation coordination equations and equilibrium equations. The methodology is validated through augmented Lagrangian (AL) method. The results can clearly illustrate the local disengagement phenome, greatly overcome the nonconvergence of the iteration, and significantly improve computing efficiency.