Safety Monitoring and Management of Reservoir and Dams

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 38350

Special Issue Editors


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Guest Editor
Institute of Water Resources and Hydro-Electric Engineering, Xi’an University of Technology, Xi’an 710048, China
Interests: reservoir management; hydraulic structure; safety monitoring; non-destructive test; data analysis
Special Issues, Collections and Topics in MDPI journals
Institute of Water Resources and Hydro-Electric Engineering, Xi’an University of Technology, Xi’an 710048, China
Interests: hydraulic engineering; numerical simulation; seismic analysis; monitoring equipment; non-destructive test
Special Issues, Collections and Topics in MDPI journals
Institute of Water Resources and Hydro-Electric Engineering, Xi’an University of Technology, Xi’an 710048, China
Interests: dam safety; discrete element method; monitoring model; machine learning; rockfill material
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Hydraulic engineering plays a vital role in the survival and development of human society, of which the actual complex operation is far beyond the imagination of engineers and scientists. For the aging structure, complex environment, and abnormal situation, the above characteristics of hydraulic structures are more prominent. Therefore, it is difficult for engineers and scientists to fully understand the safety characteristics of hydraulic structures. By applying various safety monitoring facility, data processing methods, and evaluation methods, combined with geotechnical tests, non-destructive testing, numerical simulation, intelligence algorithm, and other techniques, we can further understand the structural state of hydraulic structures. It is of great significance to improve the safety of hydraulic engineering and the development level of human society. Therefore, this Special Issue focuses on safety monitoring and the management of reservoir and dams. We would like to invite you to submit your research paper to this Special Issue.

Prof. Dr. Jie Yang
Dr. Lin Cheng
Dr. Chunhui Ma
Guest Editors

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Keywords

  • hydraulic engineering
  • safety monitoring
  • monitoring model
  • monitoring sensor
  • monitoring data analysis
  • numerical simulation
  • intelligence algorithm
  • non-destructive test
  • safety evaluation
  • risk analysis

Published Papers (20 papers)

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Editorial

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5 pages, 181 KiB  
Editorial
Safety Monitoring and Management of Reservoir and Dams
by Chunhui Ma, Xiaoyan Xu, Jie Yang and Lin Cheng
Water 2023, 15(6), 1078; https://doi.org/10.3390/w15061078 - 11 Mar 2023
Cited by 5 | Viewed by 1880
Abstract
Water conservancy projects have the functions of flood control, power generation, water supply, and irrigation, and play a vital role in the survival and development of human society [...] Full article
(This article belongs to the Special Issue Safety Monitoring and Management of Reservoir and Dams)

Research

Jump to: Editorial

22 pages, 13713 KiB  
Article
Water Distribution Characteristics of Slopes Based on the High-Density Electrical Method
by Xiaochun Lu, Xiao Liu, Bobo Xiong, Xue Cui, Bin Tian, Zhenglong Cai, Ning Shuang and Mi Zhou
Water 2023, 15(5), 895; https://doi.org/10.3390/w15050895 - 26 Feb 2023
Cited by 3 | Viewed by 1667
Abstract
Measuring the water content of slopes is essential because the distribution and migration of water within slopes are important factors of landslide instability. In this study, the relationship between the resistivity, volumetric temperature water content and temperature of landslide soil was modelled. The [...] Read more.
Measuring the water content of slopes is essential because the distribution and migration of water within slopes are important factors of landslide instability. In this study, the relationship between the resistivity, volumetric temperature water content and temperature of landslide soil was modelled. The model was validated by indoor landslide model tests and field tests in Baijiabao to investigate the effect of reservoir water levels on the water content of landslide slopes. Test results showed that, as the reservoir levels rose, the water content of the landslide soil increased. Moreover, a good correspondence between the measured results and the inversion results based on the resistivity data was obtained by using the high-density electrical method in combination with the developed model of the relationship between resistivity, volumetric water content and temperature, indicating that the proposed method is reliable and practicable in hydrodynamic landslide monitoring. Full article
(This article belongs to the Special Issue Safety Monitoring and Management of Reservoir and Dams)
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17 pages, 4451 KiB  
Article
Research on the Uplift Pressure Prediction of Concrete Dams Based on the CNN-GRU Model
by Guowei Hua, Shijie Wang, Meng Xiao and Shaohua Hu
Water 2023, 15(2), 319; https://doi.org/10.3390/w15020319 - 12 Jan 2023
Cited by 9 | Viewed by 2165
Abstract
Dam safety is considerably affected by seepage, and uplift pressure is a key indicator of dam seepage. Thus, making accurate predictions of uplift pressure trends can improve dam hazard forecasting. In this study, a convolutional neural network, (CNN)-gated recurrent neural network, (GRU)-based uplift [...] Read more.
Dam safety is considerably affected by seepage, and uplift pressure is a key indicator of dam seepage. Thus, making accurate predictions of uplift pressure trends can improve dam hazard forecasting. In this study, a convolutional neural network, (CNN)-gated recurrent neural network, (GRU)-based uplift pressure prediction model was developed, which included the CNN model’s feature extractability and the GRU model’s learnability for time series correlation data. Then, the model performance was verified using a dam as an example. The results showed that the mean absolute errors (MAEs) of the CNN-GRU model were 0.1554, 0.0398, 0.2306, and 0.1827, and the root mean square errors (RMSEs) were 0.1903, 0.0548, 0.2916, and 0.2127. The prediction performance was better than that of the particle swarm optimization–back propagation (PSO-BP), artificial bee colony optimization–support vector machines (ABC-SVM), GRU, long short-term memory network (LSTM), and CNN-LSTM models. The method improves the utilization rate of dam safety monitoring results and has engineering utility for safe dam operations. Full article
(This article belongs to the Special Issue Safety Monitoring and Management of Reservoir and Dams)
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17 pages, 5616 KiB  
Article
Dam Crack Image Detection Model on Feature Enhancement and Attention Mechanism
by Guoyan Xu, Xu Han, Yuwei Zhang and Chunyan Wu
Water 2023, 15(1), 64; https://doi.org/10.3390/w15010064 - 25 Dec 2022
Cited by 5 | Viewed by 2250
Abstract
Dam crack detection can effectively avoid safety accidents of dams. To solve the problem that the dam crack image samples are not available and the traditional algorithm detects cracks with low accuracy, we provide a dam crack image detection model based on crack [...] Read more.
Dam crack detection can effectively avoid safety accidents of dams. To solve the problem that the dam crack image samples are not available and the traditional algorithm detects cracks with low accuracy, we provide a dam crack image detection model based on crack feature enhancement and attention mechanism. Firstly, we expand the dam crack image dataset through a generative adversarial network based on crack feature enhancement (Cracks Enhancements GAN, CE-GAN). It can fully expand the dam crack data samples and improve the quality of the training data. Secondly, we propose a crack image detection model based on the attention mechanism (Attention-based Faster-RCNN, AF-RCNN). The attention mechanism is added in the crack detection module to give different weights to the proposal boxes around the crack target and fuse the candidate boxes with high weights to accurately detect the crack target location. The experimental results show that our algorithm achieves 81.07% mAP on the expanded dam crack dataset, which is 8.39% higher than the original Faster-RCNN algorithm. The detection accuracy is significantly improved compared with other traditional dam crack detection algorithm models. Full article
(This article belongs to the Special Issue Safety Monitoring and Management of Reservoir and Dams)
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19 pages, 13802 KiB  
Article
Simulation Feedback of Temperature Field of Super-High Arch Dam during Operation and Its Difference with Design Temperature
by Chunyao Hou, Dong Chai, Heng Cheng, Shaoqing Ning, Bo Yang and Yi Zhou
Water 2022, 14(24), 4028; https://doi.org/10.3390/w14244028 - 09 Dec 2022
Cited by 2 | Viewed by 1269
Abstract
Temperature is one of the main loads of super-high arch dams. (1) Background: a super-high arch dam in southwest China was taken as an example in this paper and the temporal-spatial evolution law of dam temperature was analyzed based on the monitoring data. [...] Read more.
Temperature is one of the main loads of super-high arch dams. (1) Background: a super-high arch dam in southwest China was taken as an example in this paper and the temporal-spatial evolution law of dam temperature was analyzed based on the monitoring data. (2) Methods: the finite element simulation analysis method was adopted to invert the boundary conditions of temperature on the upstream surface and the thermal parameters of the concrete, and the temperature evolution process of the arch dam in long-term operation was simulated and analyzed. After the distribution characteristics of the designed reservoir water temperature and the actual reservoir water temperature were compared, the difference in the temperature field of the arch dam during the impoundment and operation under the designed and actual conditions was studied. (3) Results: the temporal-spatial evolution law of the temperature in the dam operation period accords with the conventional knowledge, and the calculated value through simulation feedback is in good agreement with the monitoring value, which can reflect the actual temperature field distribution of the dam. (4) Conclusions: under the design condition, the dam temperature rose slowly after closure grouting and then tended to be stable. Under the actual condition, the temperature rose by 7.1~9.2 °C after closure grouting, reached the highest temperature in about 8~12 years, and fell back to a stable temperature in 40~80 years. Full article
(This article belongs to the Special Issue Safety Monitoring and Management of Reservoir and Dams)
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26 pages, 17029 KiB  
Article
Deformation Prediction of Cihaxia Landslide Using InSAR and Deep Learning
by Yuxiao Wang, Shouyi Li and Bin Li
Water 2022, 14(24), 3990; https://doi.org/10.3390/w14243990 - 07 Dec 2022
Cited by 5 | Viewed by 1447
Abstract
Slope deformation monitoring and analysis are significant in the geological survey of hydraulic engineering. However, predicting future slope deformation is a vital and challenging task for engineers. The accurate estimation of slope displacement is required for the risk assessment of slope stability. This [...] Read more.
Slope deformation monitoring and analysis are significant in the geological survey of hydraulic engineering. However, predicting future slope deformation is a vital and challenging task for engineers. The accurate estimation of slope displacement is required for the risk assessment of slope stability. This study was conducted using slope deformation data obtained by interferometric synthetic aperture radar. Five typical points of the slope in different zones were selected to establish the prediction model. Based on the observed data, a prediction model based on long short-term memory (LSTM) and autoregressive integrated moving average (ARIMA) was proposed. Firstly, ARIMA and LSTM models were used separately to predict slope deformation. Root mean square error, mean absolute error, and R2 were used to evaluate the performance of the models, and the results showed that LSTM is more effective than ARIMA. It denotes that the LSTM model can catch the trend in the data sequence with time, and ARIMA is good at predicting the bias in the stationary data sequence. Then, the predictions of ARIMA were added to the original data while the new data were fed to the LSTM model. For most data points, our LSTM-ARIMA model achieved good performance, indicating that the model is robust in slope deformation prediction. The effectiveness of the proposed LSTM-ARIMA model will enable engineers to take corresponding measures to prevent accidents before landslides occur. Full article
(This article belongs to the Special Issue Safety Monitoring and Management of Reservoir and Dams)
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13 pages, 2883 KiB  
Article
Prediction Model of Hydropower Generation and Its Economic Benefits Based on EEMD-ADAM-GRU Fusion Model
by Jiechen Wang, Zhimei Gao and Yan Ma
Water 2022, 14(23), 3896; https://doi.org/10.3390/w14233896 - 30 Nov 2022
Cited by 2 | Viewed by 1497
Abstract
As an important function of hydraulic engineering, power generation has made a great contribution to the growth of national economies worldwide. Therefore, it is of practical engineering significance to analyze and predict hydropower generation and its economic benefits. In order to predict the [...] Read more.
As an important function of hydraulic engineering, power generation has made a great contribution to the growth of national economies worldwide. Therefore, it is of practical engineering significance to analyze and predict hydropower generation and its economic benefits. In order to predict the amount of hydropower generation in China and calculate the corresponding economic benefits with high precision, Ensemble Empirical Mode Decomposition (EEMD), Adaptive Moment Estimation (ADAM) and Gated Recent Unit (GRU) neural networks are integrated. Firstly, the monitoring data of hydropower generation is decomposed into several signals of different scales by the EEMD method to eliminate the non-stationary components of the data. Then, the ADAM optimization algorithm is used to optimize the parameters of the GRU neural network. The relatively stable component signals obtained from the decomposition are sent to the optimized GRU model for training and predicting. Finally, the hydropower generation prediction results are obtained by accumulating the prediction results of all components. This paper selects the time series of China’s monthly power generation as the analysis object and forecasts the economic benefits by constructing the fusion prediction model. The RMSE EEMD-ADAM-GRU model is reduced by 16.16%, 20.55%, 12.10%, 17.97% and 7.95%, respectively, of compared with the NARNET, EEMD-LSTM, AR, ARIMA and VAR models. The results show that the proposed model is more effective for forecasting the time series of hydropower generation and that it can estimate the economic benefits quantitatively. Full article
(This article belongs to the Special Issue Safety Monitoring and Management of Reservoir and Dams)
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15 pages, 4555 KiB  
Article
Working Mode Identification Method for High Arch Dam Discharge Structure Based on Improved Wavelet Threshold–EMD and RDT Algorithm
by Yingjia Guo, Zongzhe You and Bowen Wei
Water 2022, 14(22), 3735; https://doi.org/10.3390/w14223735 - 17 Nov 2022
Cited by 3 | Viewed by 1381
Abstract
Prototype vibration response data of high arch dam discharge structures inevitably mix various noises under the discharge excitation, which adversely affects the accuracy of the working modal identification of the structure. To effectively filter noise and reduce modal aliasing for better identification accuracy, [...] Read more.
Prototype vibration response data of high arch dam discharge structures inevitably mix various noises under the discharge excitation, which adversely affects the accuracy of the working modal identification of the structure. To effectively filter noise and reduce modal aliasing for better identification accuracy, this study proposes an improved modal threshold identification method based on an improved wavelet threshold–empirical mode decomposition (EMD) and random decrement technique (RDT) algorithm for high arch dam discharge structures. On the basis of the measured vibration response data of the dam, the wavelet threshold is adopted to filter out most of the high-frequency white noise and to reduce the boundary accumulation effect of EMD decomposition. Detrended fluctuation analysis (DFA) is utilized to filter white noise and low-frequency flow noise after EMD decomposition. The natural frequency and damping ratio of the structure system are obtained by the improved RDT algorithm. The engineering examples show that the proposed method can accurately filter the measured vibration response signal noise of the discharge structure, retain the signal characteristic information, improve the accuracy of working modal recognition of the structural vibration response, avoid the complex ordering process of the system, and ease the working modal parameter identification of high arch dam discharge structures. This method can be applied to the mode identification of other large structures, as well. Full article
(This article belongs to the Special Issue Safety Monitoring and Management of Reservoir and Dams)
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11 pages, 6751 KiB  
Article
Separation Method of Main and Foreign Water for the Measuring Weirs of Danjiangkou Earth-Rock Dam
by Weihua Fang, Weiping Zhang, Chenghan Zhang, Zhiwen Xie and Tiantang Yu
Water 2022, 14(22), 3620; https://doi.org/10.3390/w14223620 - 10 Nov 2022
Cited by 3 | Viewed by 1457
Abstract
Reservoir water and rainfall are the two main factors affecting the seepage of earth-rock dams, but the seepage measurement includes the seepage discharge caused by the reservoir water and rainfall. Only by obtaining the seepage discharge caused by the reservoir water can we [...] Read more.
Reservoir water and rainfall are the two main factors affecting the seepage of earth-rock dams, but the seepage measurement includes the seepage discharge caused by the reservoir water and rainfall. Only by obtaining the seepage discharge caused by the reservoir water can we evaluate the seepage safety state. In this paper, a statistical model for seepage monitoring of earth-rock dams is established. Normal distribution function and Rayleigh distribution function are used as the lag functions of reservoir water and rainfall, respectively. The grey wolf algorithm is used to solve the lag days, and the partial least square method is adopted to solve the regression coefficient of the statistical model. Then, the reservoir water (main water) and rainfall infiltration (foreign water) parts of the measuring weir are separated with the statistical model. The developed method is used to separate the main and foreign water parts of the three measuring weirs of the Danjiangkou earth-rock dam. The results show that the overall accuracy of the models is high (the multi-correlation coefficients are about 0.95), and the separated main and foreign water seepage discharge conforms to the seepage law of earth-rock dam, which verifies the effectiveness of the method. Full article
(This article belongs to the Special Issue Safety Monitoring and Management of Reservoir and Dams)
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16 pages, 2565 KiB  
Article
Prediction of Dam Deformation Using SSA-LSTM Model Based on Empirical Mode Decomposition Method and Wavelet Threshold Noise Reduction
by Caiyi Zhang, Shuyan Fu, Bin Ou, Zhenyu Liu and Mengfan Hu
Water 2022, 14(21), 3380; https://doi.org/10.3390/w14213380 - 25 Oct 2022
Cited by 11 | Viewed by 1402
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Safety Monitoring and Management of Reservoir and Dams)
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21 pages, 4322 KiB  
Article
Debris Flow Prediction Based on the Fast Multiple Principal Component Extraction and Optimized Broad Learning
by Genqi Xu, Xin-E Yan, Ning Cao, Jing Ma, Guokun Xie and Lu Li
Water 2022, 14(21), 3374; https://doi.org/10.3390/w14213374 - 24 Oct 2022
Cited by 1 | Viewed by 1564
Abstract
In the current research of debris flow geological disaster prediction, determining reasonable disaster-inducing factors and ensuring the accuracy and rapidity of the prediction model are considered vital issues, and also, essential foundations for disaster early warning and disaster prevention and mitigation. Aiming at [...] Read more.
In the current research of debris flow geological disaster prediction, determining reasonable disaster-inducing factors and ensuring the accuracy and rapidity of the prediction model are considered vital issues, and also, essential foundations for disaster early warning and disaster prevention and mitigation. Aiming at the problems of low prediction accuracy and long prediction time in the current debris flow research, firstly, six debris flow impact factors were selected relying on the fast multiple principal component extraction (FMPCE) algorithm, including rainfall, slope gradient, gully bed gradient, relative height difference, soil moisture content and pore water pressure. Next, based on the broad learning (BL) algorithm, the debris flow prediction model based on FMPCE and the optimized BL is established with the input of debris flow-inducing factors and the output of debris flow probability. Then the model is optimized using matrix stochastic approximate singular value decomposition (SVD), and the debris flow disaster prediction model, based on SVDBL, is constructed. The prediction results of the optimized model are compared with those of the gradient descent optimized the BP neural network model(GD-BP), Support Vector Machines model(SVM) based on grid search and BL model. The results show that the accuracy of SVDBL is 7.5% higher than that of GD-BP, 3% higher than that of SVM and 0.5% higher than that of BL. The RMSE sum of SVDBL was 0.05870, 0.0478 and 0.0227 less than that of GD-BPSVM and BL, respectively; the MAPE sum of SVDBL was 1.95%, 1.66% and 0.49% less than that of GD-BPSVM and BL; the AUC values of SVDBL were 12.75%, 7.64% and 2.79% higher than those of the above three models, respectively. In addition, the input dataset is expanded to compare the training time of each model. The simulation results show that the prediction accuracy of this model is the highest and the training time is the shortest after the dataset is expanded. This study shows that the BL can be used for debris flow prediction, and can also provide references for disaster early warning and prevention. Full article
(This article belongs to the Special Issue Safety Monitoring and Management of Reservoir and Dams)
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16 pages, 4346 KiB  
Article
Internal Erosion Experiments on Sandy Gravel Alluvium in an Embankment Dam Foundation Emphasizing Horizontal Seepage and High Surcharge Pressure
by Wei Jin, Zezhi Deng, Gang Wang, Dan Zhang and Linyi Wei
Water 2022, 14(20), 3285; https://doi.org/10.3390/w14203285 - 18 Oct 2022
Cited by 4 | Viewed by 1684
Abstract
For an internally unstable soil, fine particles can move in the pore channels between coarse particles along with seepage flow; this process is termed internal erosion. To evaluate the internal stability and internal erosion behavior of sandy gravel alluvium beneath the suspended cutoff [...] Read more.
For an internally unstable soil, fine particles can move in the pore channels between coarse particles along with seepage flow; this process is termed internal erosion. To evaluate the internal stability and internal erosion behavior of sandy gravel alluvium beneath the suspended cutoff wall in an embankment dam foundation, a series of horizontal seepage tests were carried out on the four representative gradations of the alluvium layer using a large-scale high-pressure erosion apparatus. The evolutionary trends of hydraulic conductivity, the erosion ratio of fine particles, and volumetric strain under stepwise increasing hydraulic loading were obtained. The results showed that the specimens of different gradations exhibited distinct properties in permeability, particle loss, and deformation, depending on the gradation continuity and fine particle content, which can be attributed to the difference in the composition of the soil skeleton and the arrangement of coarse and fine particles. For the specimens with continuous gradations or relatively high fine particle content, the surcharge pressure can significantly improve their internal stability. By contrast, in the situations of gap-graded gradations or low fine particle content, no considerable improvement was found because the stress was mainly borne by the coarse skeleton. The practical implications of the experimental results were demonstrated by evaluating the seepage safety of the zone beneath the suspended wall in the dam foundation. Full article
(This article belongs to the Special Issue Safety Monitoring and Management of Reservoir and Dams)
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26 pages, 8796 KiB  
Article
A Literature Review and Result Interpretation of the System Identification of Arch Dams Using Seismic Monitoring Data
by Lin Cheng, Chunhui Ma, Xina Yuan, Jie Yang, Liangcai Hu and Dongjian Zheng
Water 2022, 14(20), 3207; https://doi.org/10.3390/w14203207 - 12 Oct 2022
Cited by 3 | Viewed by 1926
Abstract
The system identification of concrete dams using seismic monitoring data can reveal the practical dynamic properties of structures during earthquakes and provide valuable information for the analysis of structural seismic response, finite element model calibration, and the assessment of postearthquake structural damage. In [...] Read more.
The system identification of concrete dams using seismic monitoring data can reveal the practical dynamic properties of structures during earthquakes and provide valuable information for the analysis of structural seismic response, finite element model calibration, and the assessment of postearthquake structural damage. In this investigation, seismic monitoring data of the Pacoima arch dam were used to identify the structural modal parameters. The identified modal parameters of the Pacoima arch dam, derived in different previous studies that used forced vibration tests (FVT), numerical calculation, and seismic monitoring, were compared. Meanwhile, different modal identification results using the input-output (IO) methods and the output-only (OO) identification methods as well as the linear time-varying (LTV) modal identification method were adopted to compare the modal identification results. Taking into account the different excitation, seismic input, and modal identification methods, the reasons for the differences among these identification results were analyzed, and some existing problems in the current modal identification of concrete dams are pointed out. These analysis results provide valuable guidance regarding the selection of appropriate identification methods and the evaluation of the system identification results for practical engineering applications. Full article
(This article belongs to the Special Issue Safety Monitoring and Management of Reservoir and Dams)
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9 pages, 3369 KiB  
Article
Inspection of Cracks in the Piston Rod of a Hydraulic Cylinder Using Injected Alternating Current-Field Measurement
by Jikai Zhang, Yuewen Huang, Jian Tang, Fangfang Zhou, Yihua Kang and Bo Feng
Water 2022, 14(17), 2736; https://doi.org/10.3390/w14172736 - 02 Sep 2022
Cited by 1 | Viewed by 1654
Abstract
This paper proposes a method of injected alternating current-field measurement (IAC-FM) for detecting orthogonal cracks in the piston rod of a hydraulic cylinder in a gate hoist. Using this method, both longitudinal and transverse cracks can be detected at the same time. An [...] Read more.
This paper proposes a method of injected alternating current-field measurement (IAC-FM) for detecting orthogonal cracks in the piston rod of a hydraulic cylinder in a gate hoist. Using this method, both longitudinal and transverse cracks can be detected at the same time. An alternating magnetic field is produced inside the steel rod by axially injecting an alternating current into the rod. The longitudinal crack perturbs the circumferential magnetic field, whereas the transverse crack perturbs the current in the axial direction. Analyses of the behaviors of the magnetic field in the vicinity of the cracks were proposed, using a three-dimensional finite element software. An experimental setup was built and validation experiments were performed. The effects of the operating frequency and scan path were also studied. The results verified the feasibility of the IAC-FM method and showed great potential for the inspection of in-service hydraulic cylinders of gate hoists. Full article
(This article belongs to the Special Issue Safety Monitoring and Management of Reservoir and Dams)
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26 pages, 5937 KiB  
Article
Identification of Sensitive Parameters for Deformation of Asphalt Concrete Face Rockfill Dam of Pumped Storage Power Station
by Baotai Ma, Wenbing Zhang, Zhenzhong Shen, Donghao Zhou, Haozheng Yao and Runye Wang
Water 2022, 14(17), 2634; https://doi.org/10.3390/w14172634 - 26 Aug 2022
Cited by 1 | Viewed by 1806
Abstract
Pumped storage power station (PSPS) is an important clean energy project that plays an important role in ensuring the economical, safe, and stable operation of power systems and alleviating the contradiction of peak load regulation. Deformation analysis of the built and under construction [...] Read more.
Pumped storage power station (PSPS) is an important clean energy project that plays an important role in ensuring the economical, safe, and stable operation of power systems and alleviating the contradiction of peak load regulation. Deformation analysis of the built and under construction PSPS dam was an important process of dam design and operation, which was of great significance to ensure the safe operation of hydraulic structures in the reservoir site. Nevertheless, there were many parameters involved in the model for analyzing dam deformation, which brings a large workload to the inversion and application of model parameters. In this study, the asphalt concrete face rockfill dam (ACFRD) of a PSPS in Ningxia, China, was taken as an example, a dam deformation 3D finite element analysis model based on the Duncan–Chang E-B model was constructed, and the orthogonal test method was used. The model parameters of the main rockfill zone, secondary rockfill zone, and reservoir bottom backfill zone were taken as factors for the sensitivity analysis of horizontal displacement of dam H, vertical displacement u, and asphalt concrete face tensile strain ε. The results showed that initial bulk modulus base Kb, damage ratio Rf, and initial elastic modulus base K had a relatively higher sensitivity and had significant impacts on the calculation results, while internal friction angle φ, fraction angle reduction φ, bulk modulus index m, and elastic modulus index n had a relatively lower sensitivity, which had no significant impact on the calculation results. Therefore, when using the Duncan–Chang E-B model to analyze the deformations of a PSPS dam and asphalt concrete face, Kb, Rf, and K should be the focus. Parameters with a low sensitivity could be determined by engineering analogy so as to achieve the purpose of improving calculation efficiency under the premise of ensuring calculation accuracy. Meanwhile, these parameters should also be strictly controlled during construction. The results of this study could provide a reference for the design and safety assessment of ACFRD in PSPS. Full article
(This article belongs to the Special Issue Safety Monitoring and Management of Reservoir and Dams)
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19 pages, 5464 KiB  
Article
A Data-Driven Dam Deformation Forecasting and Interpretation Method Using the Measured Prototypical Temperature Data
by Peng He and Yueyang Li
Water 2022, 14(16), 2538; https://doi.org/10.3390/w14162538 - 18 Aug 2022
Cited by 4 | Viewed by 1537
Abstract
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. [...] Read more.
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. Full article
(This article belongs to the Special Issue Safety Monitoring and Management of Reservoir and Dams)
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9 pages, 1492 KiB  
Article
LSTM-Based Deformation Prediction Model of the Embankment Dam of the Danjiangkou Hydropower Station
by Shuming Wang, Bing Yang, Huimin Chen, Weihua Fang and Tiantang Yu
Water 2022, 14(16), 2464; https://doi.org/10.3390/w14162464 - 09 Aug 2022
Cited by 8 | Viewed by 1701
Abstract
The Danjiangkou hydropower station is a water source project for the middle line of the South-to-North Water Transfer Project in China. The dam is composed of riverbed concrete dam and earth rock dam on both banks, with a total length of 3442 m. [...] Read more.
The Danjiangkou hydropower station is a water source project for the middle line of the South-to-North Water Transfer Project in China. The dam is composed of riverbed concrete dam and earth rock dam on both banks, with a total length of 3442 m. Once the dam is wrecked, it will yield disastrous consequences. Therefore, it is very important to evaluate the dam safety behavior in time. Based on the long-term and short-term memory (LSTM) network, the deformation prediction models of the embankment dam of the Danjiangkou hydropower station are constructed. The models contain two LSTM layers, adopt the rectified linear unit function as the activation function and determine the super parameters of the models with Bayesian optimization algorithm. According to the settlement monitoring data of LD12ZT01 measuring point (dam crest 0 + 648) on the left bank of the embankment dam of the Danjiangkou hydropower station from July 2013 to March 2022, the LSTM and bidirectional LSTM models are constructed. In total, 80% of the monitoring data are taken as the training set data and 20% of the monitoring data are taken as the test set data. The mean absolute error, root mean square error and mean square error for the test set are 0.42978, 0.56456 and 0.31873 for partial least squares regression (PLSR), 0.35264, 0.47561 and 0.22621 for LSTM and 0.34418, 0.45400 and 0.20612 for bidirectional LSTM, respectively. The results show that the bidirectional LSTM model can obtain better deformation prediction value than the LSTM model and the PLSR. Then, the bidirectional LSTM model is used to predict the settlement value of LD16YT01 measuring point (dam crest 0 + 658) on the right bank, and the mean absolute error, root mean square error and mean square error for the test set are 0.5425, 0.66971 and 0.4520, respectively. This shows the bidirectional LSTM model can effectively predict the settlement value of the embankment dam of the Danjiangkou hydropower station. Full article
(This article belongs to the Special Issue Safety Monitoring and Management of Reservoir and Dams)
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23 pages, 9082 KiB  
Article
A Simplified Method for Leakage Estimation of Clay Core Dams with Different Groundwater Levels
by Chao Yang, Zhenzhong Shen, Liqun Xu and Hongjie Shen
Water 2022, 14(12), 1961; https://doi.org/10.3390/w14121961 - 19 Jun 2022
Cited by 4 | Viewed by 4632
Abstract
Clay core dams are widely applied in reservoir construction, regulating water resource and provide electric power. Leakage is a common problem in reservoir construction, and the leakage amount, which not only affects the economic benefits of the project, but also relates to the [...] Read more.
Clay core dams are widely applied in reservoir construction, regulating water resource and provide electric power. Leakage is a common problem in reservoir construction, and the leakage amount, which not only affects the economic benefits of the project, but also relates to the safety of the dam body, is difficult to estimate. According to Darcy’s law and stable seepage theory, an analytical method can be proposed to calculate the leakage of the clay core dam to gain the seepage flux in a short time. By making some reasonable assumptions, we propose formulae for seepage calculation in different conditions of the position of the groundwater levels, below or above the reservoir bottom. Both sets of formulae contain two parts of leakage calculation, i.e., leakage from the reservoir bottom and leakage from the dam body. By using the proposed analytical method, the leakage of clay core dams can be estimated considering the influence of the groundwater level. To prove the rationality of the analytical method, a simple numerical model can be established using Geo-studio 2020 to calculate the seepage flux of the clay core dam, where relative errors between numerical solutions and analytical solutions are less than 10%. To verify the feasibility in engineering applications, the proposed method was applied to calculate the seepage of a clay core dam in Sichuan, China, which was also calculated using numerical methods by establishing a three-dimensional model. The results show the rationality of the analytical method, which can strike a balance between precision and efficiency. Full article
(This article belongs to the Special Issue Safety Monitoring and Management of Reservoir and Dams)
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17 pages, 3900 KiB  
Article
The Effect of River Channel Characteristics on Landslide-Generated Waves and the Dynamic Water Pressure of the Dam Surface
by Xiaogang Zhang, Ning Shuang, Xiaochun Lu, Bobo Xiong, Huajun Ming, Zhenglong Cai and Xiao Liu
Water 2022, 14(10), 1543; https://doi.org/10.3390/w14101543 - 11 May 2022
Cited by 1 | Viewed by 1527
Abstract
In a reservoir area, impulsive landslide surges induced by slope failure may pose huge damage to the dam and the lives in the shoreline areas, which are greatly affected by river channel characteristics. In this study, water depth, the width of the water [...] Read more.
In a reservoir area, impulsive landslide surges induced by slope failure may pose huge damage to the dam and the lives in the shoreline areas, which are greatly affected by river channel characteristics. In this study, water depth, the width of the water surface, and the bending angle of river channel were chosen as the main influencing factors. The numerical method was used to investigate the influence of river channel characteristics on wave propagation and the distribution of dynamic water pressure on the dam surface. The effect mechanism was analyzed, and a prediction model considering river channel characteristics for the propagation wave height was established. Results show that water depth and the bending angle of the river channel play a positive role in the attenuation of the energy carried by landslide surges. The width of the water level mainly influences the propagation of impulse waves in the far-field area. The river channel characteristics affect the value of the dynamic pressure on the dam surface but have a minor effect on dynamic pressure distribution. The distribution of dynamic pressure on the dam is greatly influenced by the distance between the dam site and the place where the landslide occurs. Full article
(This article belongs to the Special Issue Safety Monitoring and Management of Reservoir and Dams)
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18 pages, 4709 KiB  
Article
Operation Performance and Seepage Flow of Impervious Body in Blast-Fill Dams Using Discrete Element Method and Measured Data
by Chunhui Ma, Zhiyue Gao, Jie Yang, Lin Cheng and Lei Chen
Water 2022, 14(9), 1443; https://doi.org/10.3390/w14091443 - 30 Apr 2022
Cited by 5 | Viewed by 1768
Abstract
As a high-efficiency and low-investment method of dam construction, blast-fill dams have been widely used in water conservancy, mining engineering, soil and water conservation, disaster prevention and other projects. Through collecting data on the main projects of the blast-fill dams, the characteristics and [...] Read more.
As a high-efficiency and low-investment method of dam construction, blast-fill dams have been widely used in water conservancy, mining engineering, soil and water conservation, disaster prevention and other projects. Through collecting data on the main projects of the blast-fill dams, the characteristics and development trends of blast-fill dams are analyzed in detail. Meanwhile, the design requirements of impervious bodies in the initial and reinforcement stages are systematically reviewed. Subsequently, with measured data of a typical blast-fill dam, the structural characteristics of blast-fill dams after blasting and the validity of the phreatic line height after reinforcement are analyzed using the discrete element method. We conclude that an appropriate construction schedule and flexible impervious material are critical features of the impervious body for a dam with large deformation. When the dam deformation is stable, a secondary treatment should be considered for the impervious body to improve the dam safety. The design ideas for the impervious body of blast-fill dams are also applicable to other dam types with large deformation for risk reduction, such as high rockfill dams, soft-rock dams and tailings dams, and have a certain significance for reference in the treatment of landslides and confined lakes. Full article
(This article belongs to the Special Issue Safety Monitoring and Management of Reservoir and Dams)
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