Dam Monitoring Data Research Articles

A reconstruction method for dam monitoring data based on improved singular value decomposition

A reconstruction method for dam monitoring data based on improved singular value decomposition

A Reservoir Dam Monitoring Technology Integrating Improved ABC Algorithm and SVM Algorithm

A reservoir dam is a water conservancy project with large investment and high social and economic benefits, which plays an irreplaceable role in flood control, power generation, water storage, and urban water supply. There is a risk of accidents in the process of reservoir dams, so dam monitoring is an important means to achieve the safe operation of reservoirs. In this paper, taking advantage of the high-dimensional and nonlinear characteristics of dam monitoring data samples, the fusion-improved ABC (artificial bee colony) algorithm is introduced, and the SVM (support vector machine) algorithm is used to optimize the penalty factor and kernel function parameters. The test results of the ABC and SVM algorithm are relatively stable, with small fluctuation amplitude, which can continuously monitor water level, pore water pressure, dam deformation, temperature, humidity, vibration, and other indicators is less than 10%, which is significantly lower than the standard ABC algorithm, the standard ANN algorithm, and the standard SVM algorithm. The independence and characteristics of the ABC–SVM algorithm are significantly higher, and the correlation is 0.03, the RMS (root mean square) is 0.2334, which is lower than that of the standard ABC algorithm of 0.09, and the standard ANN algorithm of 0.8. The stability of the results and performance stability are analyzed, which is greater than 90%. The ABC and SVM is used to predict the displacement and deformation law of the reservoir dam.

Research on Outlier Detection Methods for Dam Monitoring Data Based on Post-Data Classification

Safety monitoring of hydraulic structures is a critical task in the field of hydraulic engineering construction. This study developed a method for preprocessing and classifying monitoring data for the identification of gross errors in hydraulic structures. By utilizing linear regression and wavelet analysis techniques, it effectively differentiated various waveform characteristics in data sets, such as Sinusoidal Wave Cyclical, Triangular Wave Cyclical, Seasonal Cyclical, and Weakly Cyclical growth types. In the experiments for gross error identification, the 3σ algorithm, K-medoids algorithm, and Isolation Forest algorithm were applied to test the data. The results showed that the K-medoids algorithm excelled in processing Sinusoidal Wave Cyclical Data Sets; the 3σ algorithm adapted better to Triangular Wave Cyclical Data Sets; the Isolation Forest algorithm performed well in handling data sets with significant anomalies or atypical fluctuations and excelled in scenarios with strong seasonality and large data fluctuations; and for complex Weakly Cyclical Growth Data Sets, all three algorithms were less effective, indicating the potential need for more advanced analysis methods or a combination of multiple techniques. Testing on actual engineering data further confirmed the importance of using specific gross error identification techniques for special data types after data set pre-classification, providing a more effective technical solution for the safety monitoring of hydraulic structures.

Deep learning model for the deformation prediction of concrete dams under multistep and multifeature inputs based on an improved autoformer

The long-term prediction of deformation in concrete dams is a critical requirement for maintaining their structural integrity over time in practical management scenarios. While most existing models predominantly address short-term and medium-term predictions, there remains a significant challenge in establishing reliable correlations within complex long-term temporal patterns and varying environmental factors. Consequently, there is limited research on multistep prediction. To solve these problems, an Autoformer-based model for multistep and multifeature dam deformation prediction is proposed. In the proposed model, the sequence decomposition of dam monitoring data is used as the basic construction module inside the depth model. Then, Deep Automatic Correlation (Deep-AutoCorrelation) mechanism is employed to obtain the long-term dependence between dam deformation and environmental load. The encoder-decoder structure integrates time series decomposition and the Deep-AutoCorrelation to predict the multistep deformation of the dam. In the multistep deformation prediction of the dam, the improved Autoformer model demonstrates state-of-the-art performance, resulting in a 48% increase in prediction accuracy across five monitoring point datasets when compared to six benchmark models. Experimental results indicate that this method effectively addresses the challenge of predicting long-term deformation of dams subjected to complex environmental loads. It demonstrates robust capabilities in extracting long-term temporal features and avoids the increase of computational complexity caused by deeper time extraction.

A novel method for settlement imputation and monitoring of earth-rockfill dams subjected to large-scale missing data

Various missing values inevitably exist in the settlement monitoring data of prolonged operation of earth-rockfill dams, which delays or even interrupts the dam structure analytical procedure. This study aims to develop a reliable method for addressing the missing data problem and monitoring earth-rockfill dam settlement behavior. First, an imputation model, support vector regression based on the finite element method and time input (FEMTSVR), is proposed and offers superior performance when capturing complex nonlinear mappings from environmental variables to settlement on small sample data. Herein, an improved particle swarm optimization algorithm (IPSO) is developed, realizing the nonlinear adjustment of weights and parameter reduction during hyperparameter optimization. Then, this study establishes a sequential prediction model based on gate recurrent unit (GRU) networks to monitor dam behavior on the imputed and complete dataset. Eventually, the proposed method is evaluated using real-world earth-rockfill dam monitoring data with the help of statistical indicators, demonstrating its efficiency in monitoring settlement data subjected to large-scale missingness. This study provides a robust database for dam structural health monitoring, while also providing a promising framework for the safety assessment of other civil or hydraulic engineering based on raw monitoring data.

Identification of Trends in Dam Monitoring Data Series Based on Machine Learning and Individual Conditional Expectation Curves

Dams are complex systems that involve both the structure itself and its foundation. Rheological phenomena, expansive reactions, or alterations in the geotechnical parameters of the foundation, among others, result in non-reversible and cumulative modifications in the dam response, leading to trends in the monitoring data series. The accurate identification and definition of these trends to study their evolution are key aspects of dam safety. This manuscript proposes a methodology to identify trends in dam behavioural data series by identifying the influence of the time variable on the predictions provided by the ML models. Initially, ICE curves and SHAP values are employed to extract temporal dependence, and the ICE curves are found to be more precise and efficient in terms of computational cost. The temporal dependencies found are adjusted using a GWO algorithm to different function characteristics of irreversible processes in dams. The function that provides the best fit is selected as the most plausible. The results obtained allow us to conclude that the proposed methodology is capable of obtaining estimates of the most common trends that affect movements in concrete dams with greater precision than the statistical models most commonly used to predict the behaviour of these types of variables. These results are promising for its general application to other types of dam monitoring data series, given the versatility demonstrated for the unsupervised identification of temporal dependencies.

A Method for Identifying Gross Errors in Dam Monitoring Data

Real and effective monitoring data are crucial in assessing the structural safety of dams. Gross errors, resulting from manual mismeasurement, instrument failure, or other factors, can significantly impact the evaluation process. It is imperative to eliminate such anomalous data. However, existing methods for detecting gross errors in concrete dam deformation often focus on analyzing a single monitoring effect quantity. This can lead to sudden jumps in values of effect quantity caused by changes in environmental variables being mistakenly identified as gross error. Therefore, a method based on Fuzzy C-Means clustering algorithm (FCM) partitioning and density clustering algorithm (Ordering Points To Identify the Clustering Structure, OPTICS) combined with Local Outlier Factor (LOF) algorithm for gross error identification is proposed. Firstly, the FCM algorithm is used to achieve the division of measurement point areas. Then, the OPTICS and LOF algorithms are jointly utilized to determine the gross errors. Finally, the real gross errors are identified by comparing the time of occurrence of the gross errors at measurement points in the same area. Through the case study, the results indicate that the method can effectively identify spurious, gross errors in the monitoring effect quantity caused by environmental mutations. The accuracy of gross error detection is significantly improved, and the rate of misjudgment of gross errors is reduced.

An Automated Framework for the Health Monitoring of Dams Using Deep Learning Algorithms and Numerical Methods

Aiming to investigate the problem that dam-monitoring data are difficult to analyze in a timely and accurate automated manner, in this paper, we propose an automated framework for dam health monitoring based on data microservices. The framework consists of structural components, monitoring sensors, and a digital virtual model, which is a hybrid of a finite element (FE) model, a geometric model, a mathematical model, and a deep learning algorithm. Long short-term memory (LSTM) was employed to accurately fit and predict the monitoring data, while dynamic inversion and simulation were used to calibrate and update the data in the hybrid model. The automated tool enables systematic maintenance and management, minimizing errors that are commonly associated with manual visual inspections of structures. The effectiveness of the framework was successfully validated in the safety monitoring and management of a practical dam project, in which the hybrid model improved the prediction accuracy of monitored data, with a maximum absolute error of 0.35 mm. The proposed method can be considered user-friendly and cost-effective, which improves the operational and maintenance efficiency of the project with practical significance.

Identification and reconstruction of anomalous data in dam monitoring considering temporal correlation

In dam monitoring, anomalous data is often removed directly by researchers. However, some anomalous data may be due to sudden changes in the state of the dam itself and should not be removed. In this study, anomalous data in dam monitoring is divided into two categories: anomalous error data caused by anomalies in the monitoring equipment, and anomalous warning data caused by sudden changes in the state of the dam itself. Then we propose a method for identifying and reconstructing anomalous data in dam monitoring that takes into account temporal correlation. This method is able to identify and retain anomalous warning data, while removing and reconstructing anomalous error data. To determine the temporal correlation between dam monitoring parameters (e.g. water level, horizontal displacement, etc), we use association rules, and to reconstruct the removed dam monitoring data in the case of an incomplete dataset, we propose a dam monitoring data reconstruction network (DMDRN) based on generative adversarial network. On this basis and in combination with the density-based spatial clustering of applications with noise algorithm, the types of anomalous data in dam monitoring are identified, and the anomalous error data is reconstructed based on DMDRN. Our approach has been successfully validated in two experiments to identify and reconstruct anomalous data at a particular dam in China.

A Novel Imputation Model for Missing Concrete Dam Monitoring Data

To ensure the safety of concrete dams, a large number of monitoring instruments are embedded in the bodies and foundations of the dams. However, monitoring data are often missing due to failure of monitoring equipment, human error and other factors that cause difficulties in diagnosis of dam safety and failure to precisely predict their deformation. In this paper, a new method for imputing missing deformation data is proposed. First, since the traditional deformation increment speed distance index of the deformation similarity index does not take into account the fact that there is little change in deformations occurring in two consecutive days, the denominator of the index tends to be equal to zero. In this paper, an improved index for solving this problem is proposed. A combined weighting method for calculating the deformation similarity comprehensive index and the k-means clustering method is then proposed and used to classify deformation monitoring points. Subsequently, a panel data model that imputes different types of missing data is established. The method proposed in this paper can impute missing concrete dam deformation data more accurately; therefore, it can effectively solve the missing deformation monitoring data problem.

Proactive control model for safety prediction in tailing dam management: Applying graph depth learning optimization

Accurate and rapid prediction of monitoring parameters is an important means of proactively preventing safety accidents in tailings dams. Traditional monitoring model uses a single-point time-domain prediction method through feature selection, ignoring the correlation between monitoring point data. Multiple raw monitoring time series were used as direct input to the GCN-AGRU prediction model to estimate response changes in tailings dam operations. Firstly, the spatial location map of sensors in the tailings dam monitoring system was constructed to describe the sensor data in a multi-point arrangement. Then a graph neural network was used to capture the complex spatial dependencies of the sensor location map to obtain the overall safety level of the tailings dam. Secondly, the spatial features extracted from the graph convolutional neural network at different moments were fused with time series and input into a gated recurrent unit (GRU). The purpose was to use the fused features to capture the spatio-temporal correlation between time series of different monitoring locations. Finally, the temporal attention mechanism was combined to predict the dam monitoring data. Based on real data, the algorithm was compared with other algorithms to verify its accuracy and generalizability, which indicates that the model has great significance for improving the risk prevention and control of tailings dams.

A Novel Outlier Detection Method of Long-Term Dam Monitoring Data Based on SSA-NAR

Outlier generally exists in dam monitoring data which may seriously affect the accuracy of dam safety evaluation results. Aiming at the outlier detection of dam monitoring data, a novel dynamic detection method of dam outlier data based on SSA-NAR is proposed. This combined method does not depend on the effect quantity and influence quantity relationship of traditional dam safety theory and only uses the time series of effect quantity to mine the variation, which can avoid the impact of missing or abnormal of the influence quantity. The Nonlinear Autoregression (NAR) is a classical time series neural network widely used in engineering field. However, the prediction accuracy of NAR is greatly affected by the selection of model parameters, the Sparrow Search Algorithm (SSA) which is a novel model parameter solution method and can be combined with NAR to derive the optimal parameters of NAR prediction model. The outlier is identified through the analysis of the residual distribution between the predicted data and the measured data. The case study shows that when the original data does not contain outliers, the prediction accuracy of the model is high. When the outlier is included, the proposed model has good robustness which the outlier has little influence on the prediction effect. It can effectively detect the outlier in the original dam monitoring data and provide a reliable data basis for dam safety evaluation.

A Data-Driven Dam Deformation Forecasting and Interpretation Method Using the Measured Prototypical Temperature Data

Dam deformation is an intuitive and reliable monitoring indicator for dam structural response. With the increase in the service life of the project, the structural response and environmental quantity data collected by the structural health monitoring (SHM) system show a geometric growth trend. The traditional hydraulic-seasonal-time (HST) model shows poor performance in dealing with massive monitoring data due to the multidimensional data collinearity problem and the inaccurate temperature field simulations. To address these problems, this study proposes a data-driven dam deformation monitoring model for dealing with massive monitoring data based on the light gradient boosting tree (LGB) and Bayesian optimization (BO) algorithm. The proposed BO–LGB method can mine the underlying relationship between temperature changes and dam deformation instead of simple harmonic functions. Moreover, LGB is used to simulate the relationship between high-dimensional environmental quantity data and dam displacement changes, and the BO algorithm is used to determine the optimal hyperparameter selection of LGB based on massive monitoring data. A concrete dam in long-term service was used as the case study, and three typical dam displacement monitoring points were used for model training and validation. The experimental results have indicated that the method can properly consider the collinearity in variables, and has a good balance in modeling accuracy and efficiency when dealing with high-dimensional large-scale dam monitoring data. Moreover, the proposed method can explain the contribution difference between different input variables to select the factors with a more significant influence on modeling.

Anomaly Detection of Dam Monitoring Data based on Improved Spectral Clustering

<p>In response to the abnormal data mining in dam safety monitoring, and based on the traditional spectral clustering, this paper presents an anomaly detection method based on improved spectral clustering. This method applies a distance and density adaptive similarity measure. The natural eigenvalue is introduced to adaptively select the neighbors of data points, and the similarity is redefined to be combined with the natural k-nearest neighbor. Furthermore, the shared neighbor is introduced to adjust the similarity between the monitoring data samples according to the regional density. Moreover, considering the distribution of dam monitoring data, the initialization of clustering centers is optimized according to both the density and distance feature. This method can prevent the algorithm from local optimum, better adapt to the density of non-convex dataset, reduce the number of iterations, and enhance the efficiencies of clustering and anomaly detection. Taking the dam slab monitoring data as the research object, experimental datasets are formed. Experiments on these datasets further verify that the method of this paper can effectively adapt to discrete distribution datasets and is superior to the classical spectral clustering method in both clustering and anomaly detection.</p> <p> </p>

An efficient online outlier recognition method of dam monitoring data based on improved M-robust regression

Common anomaly recognition methods are easy to misjudge and miss outliers for the online monitoring data. This is a bottleneck problem that needs to be overcome in dam safety management moving toward informatization. Based on the data of nine hydropower stations along Dadu River Basin, this paper analyzed existing problems of the common anomaly identification method and an algorithm was proposed based on improved M-robust regression recognition. In this algorithm, the AR factor was introduced to avoid the defect that the traditional model cannot simulate random variables. The extreme value method and robust estimation were utilized to avoid the leverage effect. The model collapse caused by maximum measured value was avoided through improving the residual calculation model of M-robust and optimizing the weight distribution function. The maximum of the three values, residual quartile difference, discrete quartile difference, and measurement accuracy, was used as an anomaly recognition criterion to improve the evaluation criteria. The algorithm compiled was used in the Dadu River Company since 2017. The statistics showed that for the 150,000 measured values per day, the evaluation time could be within 15 min, the missed judgment rate was 0%, and the misjudgment rate was less than 2%. The proposed algorithm achieved a great improvement and can meet the needs of online outlier recognition in dam safety management.

A novel outlier detection method for monitoring data in dam engineering

The accuracy of the dam monitoring data influences the evaluation of dam safety. However, the actual monitoring data may contain outliers due to sensor failure or operation mistakes, which consequences to an inaccurate evaluation. To tackle this issue, an universal and robust approach for outlier detection for monitoring data is proposed from the perspective of image processing and artificial intelligence in this work. The monitoring data series consists of isolated-pattern outliers, clustered-pattern outliers and normal points. We transform the monitoring data into a binary image of a scatter plot at first. With Gaussian blur, the gray scales of isolated points (outliers) are reduced and hence are eliminated after Otsu binarization. Then, Cuckoo Search (CS) algorithm is utilized to obtain the most possible connection of the pixel aggregations, distinguishing the clustered-pattern outliers and the process line automatically. To detect the outliers thoroughly, we implement the image processing techniques and CS algorithm cyclically until the detection results converge. The results indicate that the proposed method has the highest detection accuracy compared with other five outlier detection methods. Moreover, the monitoring model established based on the data pre-processed by the proposed method has better fitting and predicting ability.

A Novel Method to Mitigate the Multipath Error for BDS-2 Dam Deformation Monitoring

The multipath effect on carrier-phase observations is related to the observational environment of the station, and cannot be eliminated or attenuated by the differential method. In the actual dam environment of deformation monitoring, multipath is very complicated. It is the main error source of BDS-2 for high precision data processing. In this paper, a new method called the multi-point hemispherical grid model (MHGM) is studied and evaluated. This method uses the hemispheric grid model to attenuate the influence of multipath errors. The effectiveness and improvement of the new method with respect to the popular sidereal filter (SF) method were assessed and verified by the actual dam monitoring data with only the BDS-2. The MHGM and SF approach calculates the multipath corrections from multi-days historical data, and then corrects the multipath error from the observations on the day of interest. Compared with the SF method, the MHGM can also effectively provide a graphical display of multipath error interference around the stations, which matches the surrounding observation environment. The results show that the double-differenced (DD) residuals of the MHGM for GEO/IGSO and MEO satellites are slightly better than those of SF, but the RMS of MHGM for GEO/IGSO+MEO DD residuals is improved by about 17.0%. The performance of the MHGM method for different satellite constellations is consistent and not affected by the different orbital repeat times, which could solve the “zero mean” assumption problem of the SF method. Meanwhile, the RMS of static with a 2 h interval and kinematic positioning errors of the MHGM method in the horizontal and vertical direction can be up to 1.7, 3.6 and 2.4, 8.1 mm, respectively. The MHGM model could avoid the multipath reference problem caused by different orbital repetition periods, making it more suitable for the combined multipath modeling of BDS-2 and BDS-3.

Choice of mathematic models of soils in static and seismic analyses of embankment dams

Relevance. Numerical analyses of behavior or stress-strain state (SSS) of embankment dams are usually performed using various computational programs, which use different, often complex mathematical models of soils. However, the right choice of these models is left to the program user, who usually has not enough experience in this field of knowledge, and therefore the results of these analyses are often obscure and erroneous. The aims of the work - development of recommendations for selecting the most reliable mathematical models of soils in numerical analyses of embankment dams and comparing these models with the most common models in modern world practice of their application. Methods. Deep comparative analysis of many soil models was conducted on the use of the soil models in numerical analyses of embankment dams during authors work in ICOLD Committee on Analysis and Dam Design. Results. On the basis of the evaluation of the reliability of soil models, recommendations were obtained on the choice and application of these models in the numerical analyses of SSS of embankment dams under action of static and seismic impacts; interactions between the results of these analyses and dam monitoring data were identified.

Dam monitoring data analysis methods: A literature review

Dam monitoring usually involves environmental variables (e.g., the water level and temperature) and effect variables (deformation, cracking, seepage, etc.). The associated monitoring data can reflect the trends in these variables over time and are important information for managers to understand the operational status of a dam. Therefore, research on monitoring data analysis methods is very important for monitoring dam safety. Dam monitoring data analysis methods can be divided into monitoring model, monitoring index, and abnormal value detection methods. A monitoring model takes environment variables as independent variables and effect variables as dependent variables. By studying the interactions among variables, the trends of effect variables can be learned for monitoring and prediction. A monitoring index is established to denote warning or extreme value considering the previous changes in effect variables to determine whether future changes are safe. Abnormal value detection is also an important method of finding abnormal changes in the dam state. This paper summarizes the principles, research progress, deficiencies, and development trends of these three types of monitoring data analysis methods. This review promotes research in the field of dam safety monitoring.

Treatment of Errors in Dam Safety Monitoring Data

The error processing of dam safety monitoring data is the basic work of the whole data analysis, and it is the guarantee for objectively evaluating the safety of dams. This paper summarizes the sources of error, analysis methods and research status of dam monitoring data, lists several commonly used error processing methods and the latest research progress, and compares 3σ criterion, Grubbs criterion and mathematical model method, and finally analyzes through engineering examples.