Dam Deformation Research Articles

Using the Morgenstern–Price Method and Cloud Theory to Invert the Shear Strength Index of Tailings Dams and Reveal the Coupling Deformation and Failure Law under Extreme Rainfall

It is difficult to obtain reliable shear strength parameters for the stability analysis and evaluation of tailings dams in an unstable state. In this study, the sensitivity of the shear strength index to the safety factor of a tailings dam was evaluated. The cohesion C range of a tailings earth rock dam in an unstable state is determined by the safety factor, and the Morgenstern–Price method is used for inversion. During parameter inversion, uncertainty reasoning is established based on cloud theory, which overcomes the problem that the fuzziness and randomness of the quantitative cohesion value are transferred to the qualitative concept of the safety factor. The results show that the change in cohesion C has a greater influence on the safety factor Fs of the tailings dam, and the value of parameter inversion is 8.6901 kPa. The deformation and failure of tailings dams under extreme rainfall conditions are analyzed by using the modified cohesion C value. The dam toe becomes the main response area of plastic deformation and slowly expands to the interior, showing creep deformation. The displacement field gradually transfers from the accumulated tailings to the tailings dam with the flow direction, causing erosion damage. This study provides a new idea and method for parameter inversion of the shear strength index of tailings dams and provides a reference for the disaster prediction and prevention of tailings dams subjected to extreme rainfall.

Multipoint Deformation Safety Monitoring Model for Concrete Arch Dams Based on Bayesian Model Selection and Averaging

The deformation properties of concrete arch dams are affected by the synergistic effects of multiple factors, featuring strong, multidimensional spatialtemporal evolution and distribution characteristics. This paper proposes a zoned safety monitoring model for arch dam deformation based on spatialtemporal similarity and model optimization to evaluate the deformation safety state of arch dam structures. First, zoned clustering of the deformation monitoring points at different locations of an arch dam was performed using a panel data multi-index clustering method to determine the deformation laws at different positions of the dam. Next, multipoint comprehensive displacements of the deformation properties of each zone were extracted using principal component analysis to extract the uniform deformation law of the monitoring point in each zone. Finally, we adopted Bayesian model selection (BMS) and Bayesian model averaging (BMA) for the regression model set, considering the uncertainty of the model. The engineering case study showed that BMA yielded robust and effective prediction results for the deformation of the arch dam. The analysis of the zoned deformation mechanism indicated that the deformation of the arch dam followed the general rule. The temperature component of the arch dam was mainly reflected in the middle with a hysteresis effect, and the time-dependent component was evident in both sides of the dam shoulder. The arch dam deformation safety monitoring model proposed in this study has strong robustness and interpretability, which can provide valuable technical support for analyzing the evolution of arch dam deformation properties.

A High-Robust Displacement Prediction Model for Super-High Arch Dams Integrating Wavelet De-Noising and Improved Random Forest

We present a novel deformation prediction model for super-high arch dams based on the prototype monitoring displacement field. The noise reduction processing of the monitoring data is conducted by a wavelet technique. The performance-improved random forest intelligent regression approach is then established for constructing the arch dam deformation statistical models, whose hyper-parameters are intelligently optimized in terms of the improved salp swarm algorithm. In total, three enhancement strategies are developed into the standard salp swarm algorithm to improve the global searching ability and the phenomenon of convergence precocious, including the elite opposition-based learning strategy, the difference strategy, and the Gaussian mutation strategy. A prediction example for super-high arch dams is presented to confirm the feasibility and applicability of the prediction model based on five evaluation criteria. The prediction results show that the proposed model is superior to other standard models, and exhibits high-prediction accuracy and excellent generalization performance. The stability of the proposed prediction model is investigated by artificially introducing noise strategies, which demonstrates the high-robust prediction features and provides a promising tool for predicting carbon emissions, epidemics, and so forth.

Ordinary Kriging Interpolation Method Combined with FEM for Arch Dam Deformation Field Estimation

The deformation characteristic of the arch dam can directly reflect its service performance, which can be analyzed on the basis of the dam deformation field. However, restricted by the limited number of dam monitoring points and the inhomogeneity of materials, an accurate measurement of arch dam deformation field is difficult to estimate by using the existing common methods, such as the spatial interpolation methods and the finite element method (FEM). With the aim of ensuring arch dam structure safety, the ordinary kriging interpolation method, combined with FEM, is proposed for arch dam deformation field estimation, in this study. Given the inversion of the computation parameters of the arch dam, FEM is used to calculate the basic arch dam deformation. Subsequently, the ordinary kriging interpolation method is introduced to estimate the spatial variance deformation at each point of the arch dam. One superhigh arch dam in China is selected as a case study; two additional methods are introduced as comparisons that are based on a numerical experiment and the actual monitoring data. The experimental results show that the proposed method considerably improves the accuracy and computational efficiency of the arch dam deformation field estimation and that it is of great practical importance for characterizing the deformation behavior of arch dams.

Levy flight-improved grey wolf optimizer algorithm-based support vector regression model for dam deformation prediction

Considering the strong non-linear time-varying behavior of dam deformation, a novel prediction model, called Levy flight-based grey wolf optimizer optimized support vector regression (LGWO-SVR), is proposed to forecast the displacements of hydropower dams. In the proposed model, the support vector regression is used to create the prediction model, whereas the Levy flight-based grey wolf optimizer algorithm is employed to search the penalty and kernel parameters for SVR. In this work, a multiple-arch dam was selected as a case study. To validate the proposed model, the predicted results of the model are compared with those derived from Grid Search algorithm, Particle Swarm Optimization, Grey Wolf Optimizer algorithm, and Genetic algorithm. The results indicate that the LGWO-SVR model performs well in the accuracy, stability, and rate of prediction. Therefore, LGWO-SVR model is suitable for dam engineering application.

Satellite remote sensing of UK embankment dams: influence of dam features on monitoring quality

Radar interferometry (interferometric synthetic aperture radar (InSAR)) is a satellite remote sensing technique, which offers deformation monitoring at frequent intervals with sub-centimetre accuracy. The most popular form of InSAR monitoring for infrastructure is persistent scatterer interferometry (PSI). This relies on the identification of persistent scatterers: permanent, rigid objects which can reliably backscatter a signal to the satellite receiver. As the typical UK embankment dam has grassed slopes, the majority of persistent scatterers are detected at hard infrastructure located at the dam crest. The aim of this paper is to study the influence of dam features on persistent scatterer detection and quality at Scottish Water embankment dams. Following a case study in which PSI can be shown to detect dam deformation associated with an extreme climate event (the summer 2018 heatwave), statistics from 17 Scottish embankment dams are used to determine which dam features lead to the best-quality PSI results. The type of wave protection at the crest of a dam is the biggest influence, whereas the presence of an asphalt-surfaced crest road is shown to have a surprisingly limited effect on persistent scatterer quality.

Методика геодезического мониторинга за насыпными гидротехническими сооружениями

Bulk hydraulic structures, such as dams and dams of tailings and ash dumps are very widespread and are hazardous production facilities. The article analyzes the existing methods of geodetic monitoring of bulk hydraulic structures, their advantages and disadvantages are considered. For monitoring bulk hydraulic structures, a new technique is proposed, in which the main type of geodetic monitoring is ground laser scanning, which allows the determination of both displacement vectors for individual points and areal deformations that can be analyzed using map algebra. To highlight the surface of the dam, filtering is used according to the molding method, which allows us to clear the cloud of points of laser reflections from vegetation. Since the enclosing dams are long, it is proposed to localize the scanning sites using geotomographic studies, which make it possible to identify areas of structural het-erogeneities in the body of the dam, voids and areas of highly moistened rocks. For visual inspection of hydraulic structures, it is proposed to use unmanned aerial photography, with which you can get a three-dimensional model of the body of the dam. The dam body model can be used for finite element modeling of stresses and filtration processes in the dam body. The article provides a database diagram of the "Dam deformation" software package, which allows you to store complex information about a hydraulic structure and use it to make decisions about the state of the object.

Determination of Monitoring Control Value for Concrete Gravity Dam Spatial Deformation Based on POT Model

Deformation can directly reflect the working behavior of the dam, so determining the deformation monitoring control value can effectively monitor the safety of dam operation. The traditional dam deformation monitoring control value only considers the single measuring point. In order to overcome the limitation, this paper presents a new method to determine the monitoring control value for concrete gravity dam based on the deformations of multi-measuring points. A dam’s comprehensive deformation displacement is determined by the measured values at different measuring points on the positive inverted vertical line and the corresponding weight of each measuring point. The projection pursuit method (PPM) combined with the grey wolf optimization (GWO) algorithm is used to determine the weight of each measuring point according to the spatial correlation distribution characteristics of dam deformation. The peaks over threshold (POT) model based on the extreme value theory is adopted to determine the monitoring control value with the obtained dam comprehensive deformation displacement. In addition, the POT model is improved with the automatic threshold determination method based on the criterion in probability theory and the GWO algorithm, which can avoid subjectivity and randomness in determining the threshold. The results of the engineering application show the feasibility and applicability of the proposed method.

Experimental study of deformation and stability of dams built of phosphogypsum under seismic impacts

The article presents the results of experimental studies of the model behavior under seismic influences. The conditions for ensuring the seismic resistance of phosphogypsum dams were investigated and established by testing the models on a vibration platform. The purpose was to study the deformability and stability of slopes of a given configuration built of phosphogypsum and to substantiate the possibility of using phosphogypsum to construct enclosing dams of sludge tanks located in seismic regions. The tests were conducted on small-scale models on a horizontal vibration platform in laboratory conditions. The methods of research using the vibration platform and the accepted modeling parameters were discussed. Dihydrate phosphogypsum with an optimal moisture content of 22-23% was used to construct the models. In total, 2 groups of prototypes were tested; they differed from each other only in the slope ratio. Models of the 1st group were tested without water. Models of the 2nd group were tested after wetting their materials to an average of 32 - 40%. To do this, water was filled in into the tank to the crest level, and the models stayed in this state for a week. Each model in both groups was tested for dynamic impacts with an earthquake intensity of 6-9 points. As a result of the studies on the seismic stability of dam fragments made of phosphogypsum, it was established that the enclosing dams of sludge tanks (built from phosphogypsum with optimal moisture content and its layerby-layer compaction up to 1.25 g/cm3, when the slope ratio was no steeper than m=3), have the necessary seismic resistance during earthquakes with an intensity of 7 -9 points. Based on the generalization of the data of model studies, the diagrams of the acceleration distribution in the height of the models were obtained. The diagrams of the acceleration distribution in height obtained in the experiments indicate the amplitude attenuation of the model vibrations from the base to the crest zone.

Study on the Influence of Water Level on Earth Dam Reinforced by Cut-Off Wall: A Case Study in Wujing Reservoir

The construction of a cut-off wall is a common reinforcement method for earth rock dams. At present, compared with the in-depth study on homogeneous earth dams, more and more attention is being paid to the stability and deformation of earth dams strengthened by a concrete cut-off wall. In this study, aiming at the Wujing project of the earth dam strengthened by cut-off wall, the influence of the water level rise and fall on the stability of the dam slope, the deformation of the dam body, and the crack width on dam crest were analyzed by numerical calculation and in situ measurement. The analysis results show that when the reservoir encounters a sudden drawdown, the safety factor of the dam slope decreases sharply. The faster the sudden drawdown, the faster the safety factor decreases. When the reservoir water level rises, the dam’s horizontal displacement shifts to the upstream direction, and the change of horizontal displacement of the downstream slope is significantly larger than that at the measuring point of the upstream slope. The water level of the reservoir rises, and the surface of the dam body rises, and the fluctuation of settlement deformation shows that the upstream side is larger than the downstream side, especially during the period of abrupt change in the reservoir water level. The longitudinal cracks on the dam crest show a tendency of shrinkage when the reservoir water level rises, and opening decreases with the decrease of deformation gradient increment and increases with the increase of gradient increment.

Stress Estimation of Concrete Dams in Service Based on Deformation Data Using SIE–APSO–CNN–LSTM

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.

A mathematical-mechanical hybrid driven approach for determining the deformation monitoring indexes of concrete dam

This paper aims at quantifying the uncertainty of mechanical parameters of the concrete dam based on prototype measured data, and puts forward a novel method for determining deformation monitoring indexes based on the quantification results of the uncertainty of deformation causes. Firstly, the deformation components are separated by the HST model for deformation of concrete dam. Secondly, on the basis of the separated hydraulic component and numerical simulation, the posterior distribution of main mechanical parameters involved in the finite element model (FEM) is updated by utilizing Markov chain Monte Carlo sampling method under Bayesian framework. Meanwhile, in order to improve the computational efficiency of the parameter calibration procedure, this study adopts a surrogate model based on the multi-layer perceptron algorithm to replace the finite element calculation. The training set of the surrogate model is generated by Latin hypercube sampling in the sample space and the optimum sample size is discussed. Thirdly, based on the probability inversion analysis results of mechanical parameters, the uncertainty quantification of hydraulic component is realized by forward analysis based on FEM. The uncertainty of temperature and aging component is characterized by confidence interval method. Then, the deformation monitoring indexes are established by integrating the uncertainty quantization results of three components. Finally, the feasibility of the proposed method is verified by using the long-term deformation monitoring data of Jinping-I arch dam. The results show that the monitoring indexes determined by this method is more sensitive to the abnormal measurement due to the uncertainty quantification of hydraulic component.

Spatiotemporal clustering analysis and zonal prediction model for deformation behavior of super-high arch dams

Super-high arch dams are affected by similar environmental factors, and there is some spatial and temporal correlation among the deformation measurement points, while the deformation law varies in different parts of the arch dam. Using a single-point model for the deformation points or a single spatiotemporal model for the whole arch dam may ignore the spatiotemporal correlation of the measurement points or not consider the spatiotemporal variability between regions. In this study, a novel analytical model for multi-point deformation monitoring of super-arch dams is proposed. By constructing deformation similarity characteristics to characterize the deformation similarity degree, then using the clustering by fast search and find of density peaks method (CFSFDP) to obtain the measurement point regions with similar deformation laws, the deformation laws of different deformation regions are analyzed. Then, based on the spatiotemporal integrated autoregressive moving average model (STARIMA), a spatiotemporal deformation analysis model is constructed for the deformation of super-high arch dams. Through example analysis, the fitting and prediction performance of the model is better than that of the single measurement point model and the spatiotemporal hybrid model, which provides an efficient and convenient new method for deformation monitoring and analysis of super-high arch dams.

Time Series Prediction of Dam Deformation Using a Hybrid STL–CNN–GRU Model Based on Sparrow Search Algorithm Optimization

During its long service life, an arch dam affected by a combination of factors exhibits a typical time-varying characteristic in terms of its structure and material properties, and the deformation in the dam structure can directly and reliably reflect the health and service status of dams. Therefore, an accurate deformation prediction is an important part of dam safety monitoring. However, due to multiple factors, dam deformation data often tend to be highly volatile, and most existing deformation estimation techniques employ a single algorithm, which may not effectively capture the potential change process. A hybrid model for dam deformation prediction has been proposed to overcome this problem. First, dam deformation data are decomposed into three components by seasonal and trend decomposition using loess. Second, a convolutional neural network–gated recurrent unit (GRU) hybrid model, which optimizes hyperparameters using the sparrow search algorithm, is used to capture the nonlinear relationships that exist in each component. Finally, the final prediction result of dam deformation is the comprehensive output of multiple submodules. The deformation monitoring data (period: 2009–2019) of a parabolic variable-thickness double-curved arch dam located in China are considered as the survey target. The test results indicate that the proposed model is suitable for short-term and long-term prediction and outperforms other models in terms of higher robustness to abnormal sequences than other conventional models (R² differs by 5.50% and 7.87%, respectively, in short-term and long-term predictions for different measurement points, while other models differ by 9.78% to reach 15.71%, respectively). Among the models studied, the GRU shows better robustness to abnormal series than the LSTM with good prediction accuracy, fewer parameters, and a simpler structure. Hence, the GRU can be employed for dam deformation prediction in practical engineering.

Prediction of long-term maximum settlement deformation of concrete face rockfill dams using hybrid support vector regression optimized with HHO algorithm

Prediction of long-term maximum settlement deformation of concrete face rockfill dams using hybrid support vector regression optimized with HHO algorithm

Dam deformation early warning model based on cluster analysis and spatiotemporal data fusion

The deformation behavior has a vitally spatial correlation to hydraulic concrete structures. The majority of traditional monitoring model focuses on the displacement law of local monitoring points, in which the single objective method is employed, and results in the ignorance of temporal and spatial diversity of structural behaviors. Thus, the state of the whole structure cannot be truly reflected. In this paper, the correlation between the field data obtained from different measuring points was considered, and a dam deformation early warning model based on spatiotemporal data fusion was proposed. The model was established with respect to temporal and spatial dimensions, and the correlation of monitoring points was explored by utilizing cluster analysis. The cross-sectional series fusion model was used to establish the mapping relationship between environmental factors and cross-sectional clusters for extracting the variation regulation of overall horizontal displacements or the structural deflection curve for dams during long-term operation and identifying the abnormal deformation position. Meanwhile, the time series fusion model was established based on the insight of the Stacked Single-Target strategy, in which the correlation between monitoring points was fully utilized to improve the prediction accuracy. Then, it was used to judge whether the monitoring points conform with the operation regulation in temporal dimension. Based on the early warning model in spatiotemporal dimension, a comprehensive diagnosis method of dam deformation was proposed. In addition, this method can be used for further analyzing the critical environmental factors affecting the local and overall deformation of dams. Finally, a project was selected to verify the effectiveness of the proposed model through cluster analysis and spatiotemporal data fusion.

Prediction of Dam Deformation Using SSA-LSTM Model Based on Empirical Mode Decomposition Method and Wavelet Threshold Noise Reduction

The deformation monitoring information of concrete dams contains some high-frequency components, and the high-frequency components are strongly nonlinear, which reduces the accuracy of dam deformation prediction. In order to solve such problems, this paper proposes a concrete dam deformation monitoring model based on empirical mode decomposition (EMD) combined with wavelet threshold noise reduction and sparrow search algorithm (SSA) optimization of long short-term memory network (LSTM). The model uses EMD combined with wavelet threshold to decompose and denoise the measured deformation data. On this basis, the LSTM model based on SSA optimization is used to mine the nonlinear function relationship between the reconstructed monitoring data and various influencing factors. The engineering example is analyzed and compared with the prediction results of LSTM model and PSO-SVM model. The results show that the mean absolute error (MAE) and root mean square error (RMSE) of the model are 0.05345 and 0.06358, with the complex correlation coefficient R2 of 0.9533 being closer to 1 and a better fit than the other two models. This can effectively mine the relationship in the measured deformation data, and reduce the influence of high-frequency components on the dam prediction accuracy.

Outlier Detection of Gravity Dam Deformation Monitoring Data Based on the Multiple Local Outlier Coefficient Method

In the deformation monitoring data of gravity dams, a few outliers are often found, which will have adverse effects on the monitoring of model building and other data analysis work. In this study, the multiple local outlier coefficient method was proposed to quickly detect the outlier data in real time and to provide high-quality data for subsequent data analysis. This method was based on the idea of distance in the outlier detection algorithm, aiming at laws and characteristics of gravity dam deformation monitoring data. First, the basic principle, calculation steps, and basic features of the multiple local outlier coefficient method were studied. Then, for the two important parameters of the algorithm, the appropriate window length was selected using autocorrelation and partial autocorrelation analysis, and the appropriate threshold values were selected using the 3σ criterion, maximum method, and empirical method. Finally, an engineering example was used to verify that the algorithm could accurately detect the outliers in the gravity dam deformation monitoring data, and the deviation degree and meaning of the outliers were understood according to the calculated outlier coefficient. The multiple local outlier coefficient method has the advantages of a simple calculation principle, fast calculation speed, real-time detection, and a clear meaning of calculation results. By selecting the appropriate parameters, the method could satisfy the outlier detection of different types of data, offering an advantage in adapting to the computing demand of massive monitoring data and improving the intelligence and real-time monitoring of dam safety.

Behaviour of Earth Fill Dam under Earthquake Load Using Finite Element Method: A Case Study of Semenyih Dam

In this project, an earth-fill dam in Selangor which is Semenyih Dam, a major dam in the Klang Valley with a capacity of 60.6 million cubic metres, a height of 49 metres, and a crest length of reservoir embankment of 830 metres, is being used as a study subject. The Finite Element Method was used in this work to assess the stress and deformation of Semenyih Dam, an earth dam in Selangor that was resting under hydrostatic pressure. The second objective is to study the Semenyih dam’s seismic response due to the Ranau Earthquake loading. The analysis of the Semenyih Dam’s structural performance under historical seismic loading from the Ranau Earthquake with a peak ground acceleration of 0.013g was done using the LUSAS Modeller 14.3 software. As a result, it was discovered that the dam’s deformation and displacement caused by the Ranau earthquake with hydrostatic pressure was larger, at 5.33 mm, as opposed to 1.822 mm when there was no hydrostatic pressure. The dam’s deformation is different in the two scenarios. When the Ranau earthquake combined with hydrostatic pressure, the dam deformed significantly. From the bottom of the dam to the top, there was an increase in stress distribution. Both hydrostatic pressure and an earthquake would put further strain on the dam. Semenyih Dam’s maximum stress is 189.152 N/m2 (with hydrostatic pressure), 27.4948 N/m2 (without hydrostatic pressure), which are less than the silty clay’s 290 kN/m2 allowable stress capacity. According to the investigation, when a dam is subjected to a variety of loads and forces, the stress distribution within it increases.

Semi-quantitative risk analysis of high concrete face rockfill dams under seismic action

A dam failures cause immeasurable losses, but more concrete face rockfill dams (CFRDs) are being built in areas where earthquakes occur frequently. If the risk level of CFRDs under seismic action can be identified promptly and corresponding measures taken in advance, dam failure risks can be mitigated to protect life and property. However, no study has been conducted on the seismic risk analysis of CFRDs. This study aimed to meet public security needs; hence, the risk matrix approach was used for analyzing the semi-quantitative risk of CFRDs under seismic action. Considering the seismic failure modes of dam deformation and dam slope instability, we also proposed a calculation technique of seismic failure possibility for high CFRDs. According to the worst-case scenario, the linear weighted sum method was used to assess the comprehensive seismic risk loss of CFRDs in terms of life loss,and economic loss, and socio-environmental impact. The risk levels of CFRDs under seismic action were accurately evaluated, and corresponding risk management measures were proposed according to the “As Low As Reasonably Practicable” principle. The results of this work fill the gap in the risk analysis of CFRDs under seismic action. Additionally, the results provide a theoretical basis and technical support for future seismic designs, seismic risk assessments, and risk decision-making for high CFRDs and similar projects in strong seismic areas.