Dam Monitoring Research Articles

Istraživanje opasnosti od curenja kod nasipa temeljeno na 3D električnoj tomografiji

Leakage is a common issue in reservoirs; however, accurately detecting its location and seepage diameter can be challenging owing to complex geological conditions. Three-dimensional (3D) resistivity tomography (RT), as opposed to traditional resistivity technology, offers advantages such as high observation density, efficiency, information richness, resolution, and intelligence. This study focuses on the application of 3D RT to diagnose leakage in earth and rock embankment dams. By monitoring the resistance change rate obtained from resistivity image processing, this study accurately assessed the development status of the hidden bodies. This research demonstrates that 3D RT enables rapid comparison of deformation and damage states at different moments, enabling continuous and objective monitoring of earth and rock embankment dams. This technology is comprehensive and provides real-time insights.

Forecasting and uncertainty analysis of tailings dam system safety based on data mining techniques

Tailings dams, as critical infrastructure, play a vital role in ensuring the safety and reliability of tailings pond systems. Predicting the trend of tailings dam monitoring parameters helps monitor its operational status and support safety management and decision-making. The traditional time series prediction model focuses on point prediction while neglecting the nonlinear feature of data and the uncertainty of prediction results, which is far from meeting the requirements for risk monitoring and safety management of tailings dam systems. To address this, Deep Auto-Encoder is used to extract and optimize features of multi-dimensional time series; Whale Optimization Algorithm, Long Short Term Memory, and Kernel Density Estimation are used to build a monitoring parameters trend interval prediction model. It can effectively predict and analyze the uncertainty of tailings dam monitoring parameters. Time series data of monitoring parameters are collected from a tailings dam in Anhui, China and modeled to verify the effectiveness of the model. The results show that the hybrid prediction model performs the best in monitoring parameter prediction accuracy and trend. The Root Mean Square Error values of the Whale Optimization Algorithm and Long Short Term Memory prediction for reservoir water level, surface displacement, internal displacement, phreatic line, and beach width are 0.0160, 1.0405, 0.0389, 0.0176, and 0.7525, with errors reduced by 0.37 % to 17.92 % compared to other models. The uncertainty analysis confirms the high reliability of the model. The Kernel Density Estimation effectively forecasts the fluctuation range of predicted values, significantly improving the prediction effectiveness of the monitoring parameters.

Finite Element Modelling and Simulation of Tunnel Gates of Dam Structures in ABAQUS Using Reduced-Integrated 8-Node Hexahedral Solid-Shell Element

Tunnel gates used for water regulation are very important and critical parts of dam safety. This is because they are exposed to the hydrostatic pressure of the total height of the water in the reservoir. In this case study, a nonlinear finite element modelling and simulation of the diversion tunnel gate of the Gokdere Bridge Dam in Adana, Turkey, are performed to investigate the performance and capacity of the structure before collapse. The maximum water level in the reservoir is 85 meters. A 3D finite element modelling of the gate structure was created, considering the details of the construction project. Both the concrete structure and the anchorages between the concrete of the first and second phases of the gate structure and the steel profiles in the gate supports are modelled to obtain composite behavior, bond stresses, and a more accurate load and stress distribution. For the nonlinear finite element modelling in ABAQUS, C3D8R reduced-integrated 8-node hexahedral solid elements with concrete damage and tension stiffening are used. For the simulation, linear and nonlinear capacity analyses of the gate structure are performed, and the stresses, strains, deformations, and crack propagation in concrete and steel are investigated. It is found that nonlinear analysis and finite element modelling of anchors for capacity and load transfer are important in the simulation of gate structures to prevent tunnel collapse. It is suggested that dam monitoring and control systems and the use of multiple gates are recommended when a problem occurs in the operation of a gate in the diversion tunnel gates of a dam structure.

Multisource information fusion model for deformation safety monitoring of earth and rock dams based on deep graph feature fusion

Constructing a long-term deformation monitoring model for earth–rock dams that integrates multisource monitoring information is highly important for enhancing the safety state evaluation and monitoring effectiveness of such dams. In this paper, we propose a new health monitoring model named the deformation–seepage–water level multimeasurement point health monitoring (DSW-MPHM) model for earth–rock dams based on deep graph feature fusion. This model fuses coupled seepage, deformation, and water level features from different monitoring sites of the dam body, base, and shoulder. To achieve this goal, we first establish a new module to fuse spatial and temporal features using graph convolutional networks and long short-term memory. Seepage features and water level features are then extracted using graph attention mechanisms. Subsequently, we employ the feature fusion technique, which incorporates principal component analysis and gated fusers, to construct the DSW-MPHM model, which effectively fuses information from multiple sources. This novel approach successfully addresses the issues of information redundancy and the limited reliability of monitoring models. To verify the validity of the model, it is applied to an endoscopic deformation monitoring program of a panel rockfill dam with a height of 185.5 m. The results demonstrate the superior stability and effectiveness of the proposed method compared to those of 10 baseline prediction models. Additionally, the characterization of the seepage and water level features extracted from the model is verified for its reasonableness. Thus, our proposed model is well suited for practical engineering applications.

Slope Stability Monitoring of Hydroelectric Dam and Upstream Watershed Areas Utilizing Satellite Interferometric Synthetic Aperture Radar (InSAR)

Dams offer significant benefits such as power generation, flood control, irrigation, and tourism. However, when mismanaged, they pose a high risk of catastrophic failure, potentially releasing large volumes of water rapidly, resulting in severe downstream damage, environmental impacts, injuries, and loss of lives. Effective dam monitoring is essential to evaluate slope stability, detect slope behaviour changes, and deliver early warning for potential hazards. Upon detecting any deformations in the dam or its watershed areas, prompt interventions can be initiated. Interferometric Synthetic Aperture Radar (InSAR) is the measurement technique of signal phase change between two images at the same location and at different times, it will be a proficient method for monitoring ground or slope changes across vast areas without the installation of on-site instrumentation. The dataset used in this work consists of 392 images of Sentinel-1 from 1 September 2018 to 11 August 2023 to conduct long-term InSAR data analysis. Monitoring the dam and its watershed is crucial, given that over 30% of U.S. dam failures result from overtopping. Slope failure events at the upstream area can cause abrupt water level rises, further increasing the risk of overtopping and major instability. This study explores slope deformation around dams and their watersheds using satellite long-term InSAR technology using a study case from one of the dams in Indonesia. The outcomes will provide insights into dam safety and shed light on the strengths and constraints of the InSAR technology.

A deep learning method for predicting the displacement of concrete arch dams considering the effect of cracks

Displacement represents one of the most palpable effects of dam's structural response to both internal and external influences and the monitoring of concrete arch dam displacements traditionally relied upon statistical methods, characterized by limited precision, which paid scant attention to the effect of cracks. In an endeavor to holistically consider the effect of cracks on displacements, a monitoring model, denoted as HSCT, has been devised, which integrates crack opening displacements as factors influencing displacement, establishing an initial set of influential factors. Subsequently, by employing the methods of the Max-Relevance and Min-Redundancy (mRMR) algorithm in conjunction with the Least absolute shrinkage and selection operator (Lasso), factors with the utmost predictive capacity to displacements in concrete arch dams with cracks can be identified, approaching excise detrimental features while preserving the accuracy of the model and the results are compared with the outcomes of Stepwise regression and Pearson correlation criteria for factor selection. Ultimately, a deep learning model Convolutional Neural Network with Long Short-Term Memory (CNN-LSTM) for displacement prediction is formulated. This model is applied to the filtered dataset for predictive analysis, which contrasted with Multivariate Linear Regression (MLR) as well as machine learning models (PSO-RVM and SSA-ELM). As such, it forms a deep learning method for the prediction of concrete arch dam displacements, accounting for the effect of cracks. Engineering instance substantiates the necessity of considering the influence of cracks when establishing a displacement monitoring model for concrete arch dams with cracks. Methods of mRMR and Lasso for feature selection and factor screening prove to be efficient, as they are capable of identifying a majority of crucial variables in a single computation, ensuring both accuracy and computational efficiency. CNN-LSTM model for dam displacement exhibits commendable predictive performance, adequately addressing the long-term dependencies of monitoring values while capturing short-term local characteristics. In this study, the deep learning prediction method contributes as a scientific foundation and technical support for the secure monitoring and operational management of concrete dams with cracks, introducing novel perspectives into the realm of safety monitoring and health service diagnostics for hydraulic structures with cracks.

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.

Multi-source monitoring data filtering assisted deformation analysis model updating of ultra-high rockfill dam

The deformation monitoring of ultra-high rockfill dams is now characterized by the applications of various monitoring instruments and the comparably dense measurement point deployment. The deformation analysis of rockfill dam can be greatly improved based on the multi-source monitoring data using the model updating technique. However, the model updating of rockfill dam faces challenges such as imbalanced spatial distribution of data, uneven data quality, and information redundancy, which leads to low performance and limits its application. Thus, this study proposes an innovative scheme for deformation analysis model updating, involving multi-source monitoring data filtering and model parameter identification. This scheme reduces the information redundancy, and ensures the finite element analysis (FEA) model of rockfill dam better reflects the actual deformation. The application in the 295 m Lianghekou rockfill dam demonstrates its notably enhancement of FEA accuracy, further improving the safety assessment.

Safety Monitoring Method for the Uplift Pressure of Concrete Dams Based on Optimized Spatiotemporal Clustering and the Bayesian Panel Vector Autoregressive Model

To establish a safety monitoring method for the uplift pressure of concrete dams, spatiotemporal information from monitoring data is needed. In the present study, the method of ordering points to identify the clustering structure is employed to spatially cluster the uplift pressure measuring points at different locations on the dam; three distance indexes and two clustering evaluation indexes are used to realize clustering optimization and select the optimal clustering results. The Bayesian panel vector autoregressive model is used to establish the uplift stress safety monitoring model for each category of monitoring point. For a nonstationary sequence, the difference method is selected to ensure that the sequence is stable, and the prediction is carried out according to the presence or absence of exogenous variables. The result is that the addition of exogenous variables increases the accuracy of the model’s forecast. Engineering examples show that the uplift pressure measurement points on the dam are divided into seven categories, and classification is based mainly on location and influencing factors. The multiple correlation coefficients of the training set and test set data of the BPVAR model are more than 0.80, and the prediction error of the validation set is lower than that of the Back Propagation neural network, XGBoost algorithm, and Support Vector Machines. The research in this paper provides some reference for seepage monitoring of concrete dams.

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.

Physical data-driven modeling of deformation mechanism constraints on earth-rock dams based on deep feature knowledge distillation and finite element method

Constructing an interpretable model for the long-term deformation Structural Health Monitoring (SHM) of earth-rock dams is of great significance for improving the safety state evaluation and monitoring effect. In this paper, a physics-data-driven model for the deformation SHM of earth-rock dams is proposed based on deep mechanism knowledge distillation. Firstly, the deterministic model is established based on the Finite Element Model (FEM) and outputs the hydraulic load component curve and aging component curve. Then a regression prediction model (HTSGAN) between influencing factors and deformation measurements at multiple measurement points is established based on the Graph Convolutional Network (GCN) and attention mechanism. Finally, the Teacher-Hydraulic-Time-Seepage Graph Attention Networks (T-HTSGAN) model is established based on the feature-based multi-teacher knowledge distillation using the knowledge of hydraulic loading physics and soil-rock creep physics of the FEM for mechanism constraints. The model effectively solves the problems of poor model interpretability and lack of physics knowledge constraints in previous earth-rock dam SHM models. The research results are applied to a project of a 185.5-meter-high concrete-faced rockfill dam, and the predictive performance of the model is more effective and stable through the comparison of six baseline models. The comparative analysis of the component curves proves the effectiveness of the proposed knowledge distillation method for mechanism constraints and improves the interpretability of the neural network model. Therefore, the model is more suitable for engineering applications.

Integrated application of geophysical methods in Earth dam monitoring

Integrated application of geophysical methods in Earth dam monitoring

Analysis of the percolation flow of 133 earth and rockfill dams and control values for flow meters

Worldwide, legal measures have been intensified with the aim of reducing dam accidents through government management and oversight. However, it is important to emphasize the evaluation of damages and anomalies caused by dam breaches. Geotechnical engineering currently benefits from a vast collection of data obtained through geotechnical instrumentation monitoring, which provides valuable information for the verification and validation of new projects. Utilizing this data helps in interpreting the static and dynamic behaviour of geotechnical structures, as well as supporting the adjustment and calibration of physical and computational models regarding the infiltration behaviour through dam embankments. The primary objective of this research is to consolidate real data from the monitoring history of executed and operational works in Brazil, contributing to the analysis of instrumentation through the inclusion of real flow rate values observed in flow meters. Moreover, it aims to enrich existing data in bibliographic references that assist in analysing the sensitivity of instrumentation readings. The accessed data were selected from the database of a design company, typically consisting of construction reports and historical data of instrumentation readings related to the monitoring of Brazilian dams. The results comprise consolidated real data of specific infiltration flow rates in earth and rockfill dams.

A Framework for Monitoring Stability of Tailings Dams in Realtime Using Digital Twin Simulation and Machine Learning

Tailings dam failures cause catastrophic impact on the environment and surrounding communities. Incidences of failure in the recent past have caused industrialists and researchers to seek innovative ways for proactively managing their safety and disaster mitigation. Given Industry 4.0 technologies now available, researchers are looking to develop digital tools for cost-effective, realtime monitoring of tailings dams. However, published literature indicates that a reliable framework is still lacking. This paper proposes a framework for developing a data-driven system for monitoring tailings dam stability and early warning detection. The framework relies upon digital twin simulation and machine-learning (ML) techniques, and comprises four main components: realtime data collection, digital twin modelling, ML-based early detection and prediction, and intelligence-driven decision-support. Sensors gather real-time geophysical data from monitored structure, and the digital twin uses this data to simulate dam behaviour. ML algorithms analyse the data and simulations to enable early detection of instability and failure prediction. Literature suggests that digital twin and ML-based approaches may have advantages over traditional monitoring techniques and other AI-based methods. The paper concludes with a discussion of the framework's limitations, opportunities for improvement, and potential for application in mining and geotechnical engineering. The paper serves as a basis for model development and future research.

EyeRADAR-Dam: Integration of MT-InSAR with monitoring technologies in a pilot monitoring system for embankment dams

Dams are one of the larger infrastructures of human civilization in the 21st century and subjected to increasing safety monitoring in the frame of global change. The quick development of SAR interferometry and processing capabilities allow near-real time monitoring of dams and associated areas and infrastructures. A pilot project for the surveillance and management of embankment dams, EyeRADAR-Dam has been developed in an industry-academia collaboration, supported by scientific validation. The potential of EyeRADAR-Dam lies in the integration via API of monitoring data and SAR time series dating from 1992 to present. The processed SAR data is input to the platform, together with relevant monitoring data (climate, topography, geotechnics, hydrology, flows…). A two-scale approach allows monitoring the entire reservoir, with slopes and infrastructures as a whole while simultaneously monitoring in detail critical infrastructures and dam body. EyeRADAR-Dam is part of a new generation of management platforms that integrate space data in daily operations.

The selection parameter for the operation and maintenance dam based on activity-based costing

Potential risks to people exist if a dam collapses and has a significant impact on the downstream area. Many countries are now facing the problem of having to deal with deteriorated infrastructure due to a lack of maintenance budgeting. This paper presents the dominant parameter in an Operational and Maintenance (OM) dam to build a cost estimation model to maintain the service life of the dam. The method used to identify cost-triggering parameters is based on activity-based costing and dam performance assessments using a combination of the modified Andersen, International Commission of Large Dams (ICOLD), and Dam Commission. The parameter was collected from fourteen independent variables, namely: dam height, irrigation area, sedimentation volume, grass area, wood vegetation area, corrosion area, concrete area, daily worker, corrosion expert, concrete deterioration, hydromechanical, physical performance, operation performance, and safety performance. The results of the model indicate that height, wood vegetation area, concrete maintenance area, hydromechanical deterioration, and safety performance are variables that affect OM costs. The OM costs can be reduced if the safety performance variable increases. This condition implies that if the safety performance component consists of dam monitoring activities, periodic inspections, green belt maintenance, water quality maintenance, and public awareness, the OM costs will decrease by 10%.

Macro-micro crack and damage evolution characteristics of concrete: After the action of acidic drying-saturation cycle

Repeated saturation and drying of acidic mine water have a major influence on the mechanical properties and stability monitoring of artificial dams (concrete walls) in underground reservoirs. A uniaxial compression acoustic emission (AE) monitoring test was conducted to quantify the evolution characteristics of micro cracks in concrete. In addition, the evolution characteristics of macro cracks during sample failure were quantitatively analyzed by fractal dimension theory and Image-Pro. Finally, the damage constitutive model was established and the damage evolution law was studied. The results of the study showed that the percentage of micro shear cracks in dry samples exhibited a decreasing and then increasing trend during the loading stage before the uniaxial compressive strength (UCS). However, after the acidic drying-saturation cycle (ADSC), this percentage of concrete showed an increasing trend. As the number of drying-saturation cycles increases or the pH decreases, the fractal dimension, total length, and area of the macro cracks also increase. The damage constitutive model, based on ADSC and loading conditions, works well. After the ADSC, the concrete sample will have three damage stages. Additionally, the strain at entry into each stage increases. Expansion and shrinkage of hydration products and chemical damage due to ion exchange are the mechanisms that change the mechanical properties and the macro-micro crack evolution pattern of concrete after ADSC.

DIFFERENTIAL SAR TOMOGRAPHY OF LARGE-SCALE WATER CONSERVANCY PROJECTS UNDER STEEP TERRAIN – THE CASE STUDY OF LAXIWA HYDROPOWER STATION

Abstract. Differential SAR Tomography (D-TomoSAR), as an extension of InSAR technology, combines D-InSAR and TomoSAR technology to achieve imaging in the height-deformation rate (s-v) plane. It not only solves the layover problem of SAR imaging, but also obtains the height and deformation rate of each scatterer within the image element. The technique is currently mainly applied to complex scenes in urban areas where the layover problem is serious, and the layover effect also exists for hydropower plants with extremely steep slopes. In this paper, the Differential SAR Tomography technique is applied to the four-dimensional imaging of hydraulic engineering for the first time, taking the La Siwa hydropower station as an example. This experiment establishes the signal model based on permanent scatterer points, so interferometric processing and PS point selection should be performed to obtain the differential interferogram sequence and PS points in the region; the orthogonal matching pursuit (OMP) algorithm is selected for differential SAR tomography imaging processing to reconstruct the elevation-deformation rate backward scattering profile. The experiments use the 23-view TerraSAR-X satellite one-meter resolution time series image dataset for deformation monitoring of the Laxiwa hydropower dam in GuiDe County, Qinghai Province, China, and finally the resolution and reconstruction estimation performance are evaluated by theoretical analysis and application case study. A comparison with the traditional InSAR technique shows that the differential SAR tomography technique not only maintains the advantages of high resolution, but also significantly improves the probability of accurate reconstruction of scattered points, and achieves higher accuracy in estimating the deformation of hydropower dams. This paper mainly discusses the application of differential SAR tomography technology in water conservancy projects, hoping to provide reference and help for the future large-scale application of differential SAR tomography technology in hydroelectric dam deformation monitoring.

A time series modeling approach for damage monitoring of concrete dam under seismic effects

In order to improve the applicability of time series models for the diagnosis of concrete dam damage under seismic loading, this paper proposes a diagnostic method for structural damage by combining linear time series modeling and non-linear modeling of model residuals. Starting from the linear structural dynamic response transmissibility function, the expressions of the generalized Auto-Regressive with eXtra inputs (GARX) model for the dynamic response under stationary excitation conditions were derived. The GARX model with dynamic coefficients was extended to model the dynamic response under seismic loading and the generalized Auto-Regressive Conditional Heteroscedasticity (GARCH) model was used to nonlinearly model the model residuals. To track the coefficients of the GARX model, an adaptive least squares recursion method based on fuzzy forgetting factor was studied. The damage indicator of Minkowski distance based on the GARCH model coefficients was proposed. Finally, the effectiveness of the diagnostic methods of the GARX and GARCH models was verified by a numerical calculation of the non-linear damage to a dam. The results showed that GARX can effectively describe the input-output relationship of the dam in the absence of damage. After damage occurs, the GARCH model and the improved Minkowski distance index for the model residuals of GARX can effectively characterize the non-linear damage evolution of the dam.

Intelligent Segmentation and Change Detection of Dams Based on UAV Remote Sensing Images

Guilin is situated in the southern part of China with abundant rainfall. There are 137 reservoirs, which are widely used for irrigation, flood control, water supply and power generation. However, there has been a lack of systematic and full-coverage remote sensing monitoring of reservoir dams for a long time. According to the latest public literature, high-resolution unmanned aerial vehicle (UAV) remote sensing has not been used to detect changes on the reservoir dams of Guilin. In this paper, an intelligent segmentation change detection method is proposed to complete the detection of dam change based on multitemporal high-resolution UAV remote sensing data. Firstly, an enhanced GrabCut that fuses the linear spectral clustering (LSC) superpixel mapping matrix and the Sobel edge operator is proposed to extract the features of reservoir dams. The edge operator is introduced into GrabCut to redefine the new energy function’s smooth item, which makes the segmentation results of enhanced GrabCut more robust and accurate. Then, through image registration, the multitemporal dam extraction results are unified to the same coordinate system to complete the difference operation, and finally the dam change results are obtained. The experimental results of two representative reservoir dams in Guilin show that the proposed method can achieve a very high accuracy of change detection, which is an important reference for related research.