Dam Deformation Research Articles

Outlier Detection Based on Multivariable Panel Data and K‐Means Clustering for Dam Deformation Monitoring Data

A dam is a super‐structure widely used in water conservancy engineering fields, and its long‐term safety is a focus of social concern. Deformation is a crucial evaluation index and comprehensive reflection of the structural state of dams, and thus there are many research papers on dam deformation data analysis. However, the accuracy of deformation data is the premise of dam safety monitoring analysis, and original deformation data may have some outliers caused by manual errors or instruments aging after long‐time running. These abnormal data have a negative impact on the evaluation of dam structural safety. In this study, an analytical method for detecting outliers of dam deformation data was established based on multivariable panel data and K‐means clustering theory. First, we arranged the original spatiotemporal monitoring data into the multivariable panel data format. Second, the correlation coefficients between the deformation signals of different measuring points were studied based on K‐means clustering theory. Third, the outlier detection rules were established through the changes of the correlation coefficients. Finally, the proposed model was applied to the Jinping‐I Arch Dam in China which is the highest dam in the world, and results indicate that the detection method has high accuracy detection ability, which is valuable in dam safety monitoring applications.

Наблюдений за деформацией поверхностных сооружений при строительстве метро с использованием ГНСС

Timely detection of deformations associated with geological hazards or subsidence of the ground in the sections of the construction of subways can save lives, prevent large financial obligations and avoid serious damage to the environment. Currently, Global Navigation Satellite Systems (GNSS) are considered as one of the solutions to problems in the field of monitoring and predicting deformations of buildings and structures. GNSS technologies have recently been used to monitor the deformations of high-rise buildings, bridges and dams to assess infrastructure, and the movement of the earth's crust. New improvements to the GNSS instruments allow monitoring with high accuracy in real time and with 24-hour availability.

Stress and Deformation Analysis of High Concrete Face Rockfill Dam Based on COMSOL Multiphysics

In order to study the stress and deformation characteristics of the high concrete face rockfill dam over 100 meters, in this paper, Duncan-Zhang E-B nonlinear elastic hyperbolic model is adopted, the finite element software COMSOL Multiphysics is used to establish the model and the MATLAB function is used for secondary development to analyze the stress and deformation of the concrete face rockfill dam of the Panshitou reservoir. The stress and deformation characteristics of the concrete face rockfill dam at the completion period, normal water storage condition, designed flood level condition and checked flood level condition are obtained through analysis, which provide theoretical guidance for the construction and operation of the high concrete face rockfill dam of more than 100 meters.

Device for automation of hydrostatic leveling of man-made structures with alternative power supply

The article discusses the issue of ensuring the reliability, durability, and safety of buildings and engineering, including hydraulic structures, by creating an automated hydrostatic leveling system with a device that uses fiber optics combined with an LED and a photodetector, installed on one hollow axis of rotation of the drum, like a sensor a ferromagnetic transducer was used for the displacement pulse, a comparative analysis of the existing means for measuring settlements and deformations of objects using laser and photoelectric means was carried out. A patented device has been developed that provides high accuracy, reliability, and the ability to create a monitoring system with remote information delivery with an autonomous energy source. The device can be used to install and assembly conveyor lines in determining the vertical deformations of dams, buildings, and structures during operation.

A Spatio-Temporal Dam Deformation Zoning Method Considering Non-Uniform Distribution of Monitoring Information

Deformation is the most intuitive indicator of the actual working status of a concrete dam. Zoning the variation regulation of dam deformation is one of the key parts of dam safety evaluation and risk assessment. However, the sample points reflecting deformation and variation characteristic information are non-uniformly distributed, thus it is difficult to cluster the data samples by traditional clustering methods. To solve this problem, a spatio-temporal zoning method of dam deformation considering non-uniform distribution of monitoring information is proposed. Firstly, the preprocessed deformation data are utilized to establish the similarity-distance zoning indicators using the absolute deformation, the deformation increase and the relative deformation increase respectively; then the deformation data are transferred into the Cartesian coordinate system, known as sample points. Secondly, utilize the improved Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to cluster the points. The clustering parameters $M$ and $\delta $ are determined by an optimization algorithm with an evaluation index as the objective function, then the sample points representing time sections or spatial monitoring points are clustered through dynamically updating the neighborhood radius value $\varepsilon $ . Moreover, several artificial data sets are selected to demonstrate that the improved DBSCAN algorithm is with more obvious superiority in non-uniform clustering compared to traditional algorithms. Deformation data of an existing concrete dam are presented and discussed to validate the established zoning method.

Assessment of the influence of water - level elevation in the reservoir on settlement of the hydroelectric dam

Elevation of the water level in the reservoir is the main reason for the deformation of the hydropower dams. Thus, assessing the influence of this cause on the displacement of construction is extremely necessary, which helps understand the state of dam and evaluate its safety to prevent sudden accidents that damage people and properties. Therefore, the article researches theoretical basis and builds the procedure of data processing in the algorithm of least squares to calculate the subsidence value caused by the influence of water - level elevation on the dam. The monitoring data of Yaly hydroelectric dam at two positions M5 and M26 was used in the experimental calculation. The results indicated that when the highest water level is 515.53 m, the subsidence values caused by this factor at M5 and M26 were 5.2 mm and 0.2 mm, respectively. On the other hand, when the lowest water - level is 491.2 m, the subsidence values caused by this factor at M5 and M26 were -7.7 mm and -1.9 mm, respectively. This demonstrates that the proposed calculating method is entirely feasible, reliable, and suitable for every hydroelectricity dam.

Evaluation Method for Cohesive Crack Propagation in Fragile Locations of RCC Dam Using XFEM

Roller compacted concrete (RCC) dams own a large number of horizontal construction layers, which can easily lead to weak joints among layers and generate interlayer joints with different scales to reduce the dam bearing capacity. In this study, extended finite element method (XFEM) is used to simulate crack propagation, the finite element description is first taken on the strong discontinuity. Subsequently, the displacement function of the crack-tip in the quadrilateral element and the geometric determination method of the crack-tip strengthening region are established. Afterwards, the discrete form of the governing equation is derived and the XFEM increment discretization method of the cohesive crack with the crack-tip reinforcement is proposed using the virtual node method to represent the discontinuity of the fracture element. These methods are validated through simulating mixed-mode cracking of one-sided notched asymmetric four-point bending beam. Eventually, the proposed methods are applied to RCC gravity dam to study the development rule and propagation path of the interlayer joints, so as to evaluate the effect of different lengths of the interlayer joints on the dam structural performance. The estimated critical values of dam deformation are helpful to prevent the dam failure during long term operation.

Spatiotemporal hybrid model for concrete arch dam deformation monitoring considering chaotic effect of residual series

Single-measuring point deformation monitoring model is the most popular method in dam health monitoring. Considering that single-point monitoring model cannot comprehensively reflect the overall deformation properties of dams, a spatiotemporal hybrid model of multi-point deformation monitoring for concrete arch dams is proposed. Meanwhile, considering the chaotic effect of residual series, the support vector machine optimized by particle swarm optimization algorithm (PSO-SVM) is adopted to analyze and forecast the residual series. Hence, a spatiotemporal hybrid model for concrete arch dam deformation monitoring considering the chaotic effect of residual series is proposed in the study. Based on the theory of single-measuring point deformation monitoring, a spatiotemporal hybrid model is established by introducing space coordinate and calculating hydraulic component with finite element method. Then, with the good nonlinear processing ability of PSO-SVM, the chaotic effect of residual series is analyzed and predicted by PSO-SVM. Subsequently, a spatiotemporal hybrid model for concrete arch dam deformation monitoring considering chaotic effect of residual series is established by superimposing the residual prediction term with the predicted value of the spatiotemporal hybrid model. Engineering example show that the proposed model has better fitting and predicting precisions compared with the conventional single-point monitoring models, and it can analyze and predict the deformations of multi-point simultaneously. In addition, the proposed model reduces the workload of modelling point by point in single-point monitoring model, which considerably improves the practicality and computational efficiency of deformation-based health monitoring of concrete arch dams.

Improving Dam Deformation Analysis Using Least-Squares Variance Component Estimation and Tikhonov Regularization

AbstractSeveral least-squares adjustment techniques were tested for dam deformation analysis. Deformation monitoring had only been studied horizontally. Simulated observations were first performed ...

Prediction of arch dam deformation via correlated multi-target stacking

Majority of the existing dam deformation monitoring models focus on the prediction of individual displacement, and ignore the spatial correlation of data. In this study, we propose a method dealing with multi-target prediction called the Maximum Correlated Stacking of Single-Target. The proposed method can provide reliable predictions of multi-target simultaneously, while fully exploiting the internal relationships between target variables via the strategy of targets stacking. Moreover, it can be coupled with different existing baseline models for the prediction and anomaly detection of arch dam deformation.Jinping–I arch dam is taken as a case study, where the monitoring displacement of 23 different points are analyzed and modeled simultaneously. Three kernel-based machine learning algorithms (i.e., support vector machine, relevance vector machine, and kernel extreme learning machine) and the partial least squares regression are adopted as baseline models for multi-target regression methods. Compared with the single-target regression and two state-of-the-art multi-target regression methods, the simulated results reveal the higher accuracy of the proposed method. Furthermore, model performance is validated in terms of anomaly detection capability, where two progressive anomalous scenarios (i.e., anomalies of single or multiple points) are investigated. The proposed method can be adapted for the health monitoring of other infrastructures in which multiple responses (e.g., displacement, temperature, or stress) need to be predicted simultaneously.

On the Use of an Improved Artificial Fish Swarm Algorithm-Backpropagation Neural Network for Predicting Dam Deformation Behavior

Dam behavior is difficult to predict due to its complexity. At the same time, dam deformation behavior is vital to dam systems. Developing a precise prediction model of dam deformation from prototype data is still challenging but determinant in the structural safety assessment. In this paper, an artificial neural network (ANN), trained by the improved artificial fish swarm algorithm (IAFSA) and backpropagation algorithm (BP), is proposed for predicting the dam deformation. Initially, crossover operator is embedded into AFSA, which aims to enhance the performance. In light of the influence mechanism of many factors on dam deformation behavior, the hybrid (IAFSA and BP) model uses statistical input to obtain the optimal connection weights and threshold values of the neural network. The hybrid model integrates IAFSA’s strong global searching ability and BP’s strong local search ability. To avoid overfitting the training set data, a validation set is adopted to check the generalization capability. Subsequently, the obtained optimal parameters are applied to predict the dam deformation behavior. The hybrid model’s preciseness is verified against the radial displacements of a pendulum in a concrete arch dam and simulations of four models: statistical model, BP-ANN optimized by genetic algorithm (GA), particle swarm optimization (PSO), and AFSA. Results demonstrate that the proposed model outperforms other models and may provide alarms for safety control.

Research on the application of improved SVM model in dam displacement and deformation prediction

Aiming at the influence of dam deformation on many factors, the kernel principal component analysis method is used to compare the factors affecting dam deformation, and the main factors affecting dam deformation are determined. The kernel function of support vector machine is selected, the main influence factor is input, the dam deformation is output, and the optimal parameters are obtained by grid search and cross-verification to establish the model. The deformation results are studied and analyzed, and the prediction effect is evaluated by absolute error and RMSE, and compared with the standard SVM model and the Prophet method based on time series. The results show that the accuracy of support vector machine model is improved obviously by optimizing parameters such as radius of kernel function and penalty factor through mesh optimization, and the prediction accuracy of nonlinear small sample data is significantly higher than that of time series method.

Deep learning model of concrete dam deformation prediction based on CNN

The concrete dam deformation prediction model is a key measure to predict the evolution of structural behavior and evaluate the safe service status. This paper uses open-source deep learning framework TensorFlow as the platform and uses the mature convolutional neural network technology in deep learning theory to establish the concrete dam deformation safety prediction model based on a deep learning. The application of engineering examples shows that the residual map, mean square error, and average percentage error are used as the model fitting and prediction accuracy evaluation standards. Compared with the shallow neural network model and the traditional Statistical model, the concrete dam deformation prediction model based on deep learning has higher prediction accuracy and more stable performance, providing a new method for concrete dam deformation monitoring.

Modified dam deformation monitoring model considering periodic component contained in residual sequence

Modified dam deformation monitoring model considering periodic component contained in residual sequence

Statistical modelling for high arch dam deformation during the initial impoundment period

Although statistical models are efficient in most cases to analyze concrete dam displacements, these models are built on several hypotheses, leading to uncertainties especially for special periods. The special statistical models, improving estimations of the non-stationary thermal and the non-monotonic time-dependent effects, are proposed for the displacements of high arch dams during their initial impoundment periods in this paper. The hierarchical clustering on principal component analysis is developed to divide thermometers into groups and to choose representative thermometers or identify major principal components on measured temperature data to represent the non-stationary thermal effect on dam's displacements. The non-monotonic formula for the time-dependent deformation, emphasizing the creep and its restoration of dam concrete and its surrounding rock, is derived and further simplified when the reservoir water level exhibits an evident periodicity. Then, two improved statistical models accounting for these non-stationary thermal and non-monotonic time-dependent effects are proposed. The proposed statistical models with clear physical meanings are applied to investigate the measured displacements of the Xiluodu arch dam. Model performance comparisons indicate that the proposed models have significant improvement in fitting precision and prediction ability over the traditional and more recent models. Model results confirm the influence of reservoir thermal stratification and concrete temperature rise on the thermal displacement, and the non-monotonic effect on the time-dependent displacement. The proposed models yield to a better identification of the deformation mechanism for high arch dams during their initial impoundment periods.

A Hybrid Model Integrating Principal Component Analysis, Fuzzy C-Means, and Gaussian Process Regression for Dam Deformation Prediction

Dam behavior prediction model is a fundamental component of dam structural health monitoring systems. As the most intuitive monitoring indicators, deformation is commonly used to reflect the dam behavior change. The selection of input variables and training samples determines the performance of dam deformation predictive models. In this paper, a novel hybrid model integrating principal component analysis (PCA), fuzzy C-means (FCM), and Gaussian process regression (GPR) are proposed to predict dam deformation. Specifically, PCA is utilized to extract the main information of original thermometer data as temperature variables, while FCM is used to divide the samples into several categories according to the similarity of the environmental monitoring data. Then, the samples in each category are used to train GPR models with five commonly used covariance functions based on influencing factors, respectively. In the test phase, FCM is used to determine what category the samples in the test set belong to, and then, the corresponding trained GPR model is utilized to predict dam deformation. The proposed hybrid model is fully demonstrated and validated by monitoring data collected from a multiple-arch concrete dam in long-term service. Various benchmark models with or without FCM analysis are selected as comparison models. Experimental results show the proposed novel model outperforms the other comparison methods in terms of all evaluation indicators. This indicates fuzzy clustering analysis can effectively improve the performance of the prediction model, and the proposed hybrid model can predict future dam deformation with high accuracy and efficiency.

Seismic stability and failure process analysis of earth-filled dam

Earthquake is one of the primary influence factors for the stability and safety of the earth-filled dam. An efficient way for the seismic stability and failure process analysis of the earth-filled dam is proposed in this paper. In this study, taking the dynamic mechanical behavior of soil and the ground motion characteristics into account, the seismic stability, and failure process of a two-dimensional earth-filled dam is analyzed via the explicit finite element method. A dynamic visco-elasto-plastic constituted model for soil is established and written in FORTRAN as a user subroutine of the finite element method code ABAQUS. The seismic response, residual deformation, and failure process of the earth-filled dam under natural earthquake conditions are analyzed using the dynamic visco-elasto-plastic constituted model and the explicit finite element method. Moreover, the influence of the ground motion characteristics on the seismic stability of the earth-filled dam is analyzed via the specified elastic response acceleration spectrum. The analysis results of this paper suggest that the seismic stability and the progressive failure process analysis of the earth-filled dam under the seismic condition can be analyzed efficiently by explicit finite element method. The dynamic mechanical behavior of soil and the ground motion characteristics are both critical influence factors for the seismic stability analysis of the earth-filled dam. The research work of this study is helpful for the analysis of the failure process of the earth-filled dam collapse.

A Novel Hybrid Decomposition—Ensemble Prediction Model for Dam Deformation

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.

An optimized combination prediction model for concrete dam deformation considering quantitative evaluation and hysteresis correction

Certain degree of deformation is natural while dam operates and evolves. Due to the impact of internal and external environment, dam deformation is highly nonlinear by nature. For dam safety, it is of great significance to analyze timely deformation monitoring data and be able to predict reliably deformation. A comprehensive review of existing deformation prediction models reveals two issues that deserves further attention: (1) each environmental influencing factor contributes differently to deformation, and (2) deformation lags behind environmental factors (e.g., water level and air temperature). In response, this study presents a combination deformation prediction model considering both quantitative evaluation of influencing factors and hysteresis correction in order to further improve estimation accuracy. In this study, the complex relationship in deformation prediction is effectively captured through support vector machine (SVM) modeling. Furthermore, a modified fruit fly optimization algorithm (MFOA) is presented for SVM hyper-parameter optimization. Also, a synthetic evaluation method and a hysteresis quantification algorithm are introduced to further enhance the MFOA-SVM-based model in regards to contribution quantification and phase correction respectively. The accuracy and validity of the proposed model is evaluated in a concrete dam case, where its performance is compared with other existing models. The simulated results indicated that the proposed nonlinear MFOA-SVM model considering both quantitative evaluation and hysteresis correction, abbreviated as SEV-MFOA-SVM, is more accurate and robust than conventional models. This novel model also provides an alternative method for predicting and analyzing dam deformation and evolution behavior of other similar hydraulic structures.

Efficiency of ground motion intensity measures with earthquake-induced earth dam deformations

In a seismic hazard analysis (SHA), the earthquake loading level should be predicted for one or more ground motion intensity measures (IMs) that are expected to relate well with the engineering demand parameters (EDPs) of the site. In this study, the goal was to determine the IMs that best relate to embankment dam deformations based on nonlinear deformation analysis (NDA) results of two embankment dams with a large suite of recorded ground motions. The measure utilized to determine the “best” IM was standard deviation in the engineering demand parameter (e.g., deformation) for a given IM, also termed “efficiency.” Results of the study demonstrated that for the NDA model used, Arias intensity (AI) was found to be the most efficient predictor of embankment dam deformations. In terms of pseudo-spectral acceleration (PSA)-based IMs, the PSA at short periods and then in the general range of the natural period of the dams was seen to be the most efficient IM, but was in almost all cases not as efficient as AI.