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.

Deformation prediction is an important part of concrete dam safety monitoring. In recent years, the random forest (RF) algorithm has attracted more and more attention in the field of dam safety monitoring because of its fast speed and strong generalization ability. However, the performance of RF is easily affected by many factors, such as the drift of measured value in displacement and the inappropriate setting of parameters of RF. To solve the above problems, the indicator variable model (IVM) is used to identify and eliminate the drift of measured values in this paper, and the sand cat swarm optimization (SCSO) is applied to optimize RF for the first time. On the grounds of this, a deformation prediction system of a concrete dam based on the IVM and RF algorithm optimized by SCSO is proposed. The case study shows that IVM can correct the interference of monitoring data accurately, and the maximum error rate is less than 3%; in the aspect of parameter optimization of RF, the results of the SCSO algorithm are obviously better than those of the TAE method and PSO algorithm, and the corresponding OOB error is the minimum; in terms of prediction performance, compared with TAE-RF, PSO-RF, LSTM and SVM, SCSO-RF has higher accuracy and stronger stability, and its SSE and MSE are reduced by at least 91%, MAE and RMSE are reduced by at least 71%, and R² is very close to 1. The results of study provide a new method for the automatic online evaluation of dam safety performance.

During the operation period, the deformation of an ultra-high arch dam is affected by the large fluctuation of the reservoir water level. Under the dual coupling of the ultra-high dam and the complex water level conditions, the traditional variational analysis method cannot be sufficiently applied to its deformation analysis. The deformation analysis of the ultra-high arch dam, however, is very important in order to judge the dam safety state. To analyze the deformation law of different parts of an ultra-high arch dam, the panel data clustering theory is used to construct a Spatio-temporal characteristic model of dam deformation. In order to solve the difficult problem of the fluctuating displacement of dam deformation with water level effect, three displacement component indexes (absolute quantity, growing, and fluctuation) are proposed to characterize dam deformation. To further optimize the panel clustering deformation model, the objective weight coefficient of clustering comprehensive distance is calculated based on the CRITIC (CRiteria Importance Through Inter-criteria Correlation) method. The zoning rules of the ultra-high arch dam are established by using the idea of the CSP (Constraint Satisfaction Problem) index, and the complex water level of the reservoir is simulated in the whole process. Finally, the dynamic cluster analysis of dam deformation is realized. Through a case study, three typical working conditions including the rapid rise and fall of water level and the normal operation are calculated, and the deformation laws of different deformation zones are analyzed. The results show that the model can reasonably describe the deformation law of an ultra-high arch dam under different water levels, conveniently and intuitively select representative measuring points and key monitoring parts, effectively reducing the analysis workload of lots of measuring points, and improve the reliability of arch dam deformation analysis.

The seepage of the dam is an important representation of the operation characteristics of the dam, and there are many factors affecting the seepage with a certain lag. It is still difficult to predict its change and sensitivity because of complex operating conditions. At present, the lag-sensitivity of influence factors of the dam seepage has not been studied. The time series influence factors of seepage are determined by HTRT (hydrostatic-thermal-rainfall-time) model in this paper. To avoid the pseudo fitting of conventional methods, HTRT model nested random forest algorithm is used to establish the predicting model of the dam seepage. And MIC algorithm is used to achieve the dual purposes of time lag and sensitivity analysis. Firstly, the time lag of relationship between seepage and its influencing factors is characterized, and the most appropriate lag time of the HTRT model factors is determined. Secondly, independent correlation analysis on all influencing factors is carried out and the sensitivity of each factor is analyzed by MIC. Meanwhile, the sensitivity of the factors to seepage is quantitatively analyzed by the two parameters of %IncMSE and IncNodePurity of RF algorithm. The sensitivity of influencing factors is analyzed by comparing MIC results with RF results. Combined with the case, taking the error of fitting prediction as the evaluation index of seepage prediction, the prediction accuracy of MIC-RF model, RF model and MIC-BPNN (Back Propagation neural network) model is calculated and compared. Case study showed that MIC- RF monitoring model has high prediction accuracy, strong adaptability and high robustness in dam seepage, and the sensitivity and time lag of influencing factors can be quantitatively analyzed. The water pressure and rainfall of the lag time are 14 days and 16 days respectively. The sensitivity study of the time series influencing factors of seepage showed that the water pressure component is the main controlling factor of seepage, and rainfall component is more sensitive to later stage. The MIC-RF model can be used as a new dam seepage safety monitoring model.

Investigating the dynamic response patterns and failure modes of concrete gravity dams subjected to strong earthquakes is a pivotal area of research for addressing seismic safety concerns associated with gravity dam structures. Dynamic shaking table testing has proven to be a robust methodology for exploring the dynamic characteristics and failure modes of gravity dams. This paper details the dynamic test conducted on a gravity dam model on a shaking table. The emulation concrete material, featuring high density, low dynamic elastic modulus, and appropriate strength, was meticulously designed and fabricated. Integrating the shaking table conditions with the model material, a comprehensive gravity dam shaking table model test was devised to capture the dynamic response of the model under various dynamic loads. Multiple operational conditions were carefully selected for in-depth analysis. Leveraging the dynamic strain responses, the progression of damage in the gravity dam model under these diverse conditions was thoroughly examined. Subsequently, the recorded acceleration responses were utilized for identifying dynamic characteristic parameters, including the acceleration amplification factor in the time domain, acceleration response spectrum characteristics in the frequency domain, and modal parameters reflecting the inherent characteristics of the structure. To gain a comprehensive understanding, a comparative analysis was performed by aligning the observed damage development with the identified dynamic characteristic parameters, and the sensitivity of these identified parameters to different levels of damage was discussed. The findings of this study not only offer valuable insights for conducting and scrutinizing shaking table experiments on gravity dams but also serve as crucial supporting material for identifying structural dynamic characteristic parameters and validating damage diagnosis methods for gravity dam structures.

The stress behavior of key parts of concrete dams is related to the safe operation of the dam. However, the stress sensors in concrete are susceptible to aging and failure with increasing service life. Estimating the structural stress under sensor failure or data loss scenarios for concrete dams in service is essential and complex. This study presents a stress estimation method driven by the observation data. Firstly, a one-to-one correspondence exists between dam deformation reflecting the load effect and structural stress. Estimating the structural stress by simulating load effects with dam deformation is more convenient when it is hard to simulate complex load effects directly. Therefore, based on the observed data before stress sensor failure, the spatial–temporal relationship between structure stress and multi-point deformations of a concrete dam is developed using convolutional neural networks (CNN) and long short-term memory (LSTM). An improved particle swarm optimization algorithm combined with swarm information entropy (SIE–APSO) is proposed simultaneously for tuning the network’s hyperparameter and accelerating the convergence. Finally, the stress estimation of the target part of the concrete dam in service is obtained. The case shows that it is valid and feasible. The RMSE decreased by approximately 21–58%, MAPE decreased by 19–58%, and ARV decreased by 22–94% compared with the load-stress relationship model.