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Remote Sensing, Artificial Intelligence and Deep Learning in Hydraulic Structure Safety Monitoring

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydraulics and Hydrodynamics".

Deadline for manuscript submissions: closed (20 May 2024) | Viewed by 25031

Special Issue Editors

Dr. Chenfei Shao

E-Mail Website
Guest Editor
College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China
Interests: smart dam construction; digital twin technology, dam safety monitoring; hydraulic structure; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Hao Gu

E-Mail Website
Guest Editor
College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210024, China
Interests: hydraulic structures; concrete dam; dam health diagnosis; dam safety monitoring; forecasting and early warning
Special Issues, Collections and Topics in MDPI journals
Dr. Yanxin Xu

E-Mail Website
Guest Editor
College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China
Interests: dynamic structural analysis; vibration response analysis; machine learning; oblique photography; hydraulic engineering safety monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Huixiang Chen

E-Mail Website
Guest Editor
College of Agricultural Science and Engineering, Hohai University, Nanjing 211100, China
Interests: CFD simulation; numerical simulation; computational fluid dynamics; me-chanical engineering; waste to energy; intelligent water conservancy
Special Issues, Collections and Topics in MDPI journals
Dr. Xiangnan Qin

E-Mail Website
Guest Editor
Affiliation: Division of Water Conservation and Hydropower Engineering, Zhengzhou University, Henan 450001, China
Interests: dam safety monitoring; statistical modelling; feature selection; intelligence algorithm; oblique photography; numerical simulation
Special Issues, Collections and Topics in MDPI journals
Dr. Guang Yang

E-Mail Website
Guest Editor
School of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
Interests: structural health monitoring; risk analysis; structural behaviour analysis; artificial intelligence algorithm; data processing

Special Issue Information

Dear Colleagues,

With the gradual transformation of hydraulic engineering from digitization and intelligence to wisdom, remote sensing technology, artificial intelligence and deep learning methods have been widely used for automatic perception, processing, storage and analysis of hydraulic structure engineering monitoring data. The advent of remote sensing technologies such as three-dimensional tilt photography offers the opportunity to build an integrated hydraulic engineering monitoring and acquisition system capable of capturing all the details of hydraulic engineering. With the introduction of artificial intelligence and deep learning methods, the hydraulic engineering information was analysed and exploited efficiently. Combined with the traditional hydraulic structure behaviour analysis methods, such as geotechnical testing and numerical simulation, artificial intelligence and deep learning methods can help solve more complex hydraulic engineering problems by providing more accurate and professional intelligent analysis and ubiquitous hydraulic engineering services of great theoretical importance and application value in order to achieve the general improvement of safety monitoring of hydraulic structures. Therefore, this Special Issue will focus on artificial intelligence, deep learning methods and remote sensing technologies in the safety monitoring of hydraulic structures. We would like to invite you to submit your research papers to this Special Issue. Suitable topics include, but are not limited to, the following: information perception of hydraulic structure engineering, intelligent processing methods of monitoring data, positive and inverse analysis of hydraulic structures, safety monitoring models and systems of hydraulic engineering.

Dr. Chenfei Shao
Dr. Hao Gu
Dr. Yanxin Xu
Dr. Huixiang Chen
Dr. Xiangnan Qin
Dr. Guang Yang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • remote sensing
  • artificial intelligence
  • deep learning
  • hydraulic structure
  • safety monitoring
  • data perception
  • data fusion
  • data processing
  • safety monitoring model
  • comprehensive diagnosis

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Published Papers (16 papers)

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Editorial

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6 pages, 172 KiB  
Editorial
Research Progress in Remote Sensing, Artificial Intelligence and Deep Learning in Hydraulic Structure Safety Monitoring
by Chenfei Shao, Hao Gu, Yanxin Xu, Huixiang Chen, Xiangnan Qin and Guang Yang
Water 2024, 16(16), 2230; https://doi.org/10.3390/w16162230 - 7 Aug 2024
Viewed by 1144
Abstract
Ensuring the safety of hydraulic structure engineering is of paramount importance, as these infrastructures play a critical role in water management, flood control, and the provision of clean water for various human and ecological needs [...] Full article
(This article belongs to the Special Issue Remote Sensing, Artificial Intelligence and Deep Learning in Hydraulic Structure Safety Monitoring)

Research

Jump to: Editorial, Review

26 pages, 9229 KiB  
Article
A Novel Artificial Intelligence Prediction Process of Concrete Dam Deformation Based on a Stacking Model Fusion Method
by Wenyuan Wu, Huaizhi Su, Yanming Feng, Shuai Zhang, Sen Zheng, Wenhan Cao and Hongchen Liu
Water 2024, 16(13), 1868; https://doi.org/10.3390/w16131868 - 29 Jun 2024
Viewed by 954
Abstract
Deformation effectively represents the structural integrity of concrete dams and acts as a clear indicator of their operational performance. Predicting deformation is critical for monitoring the safety of hydraulic structures. To this end, this paper proposes an artificial intelligence-based process for predicting concrete [...] Read more.
Deformation effectively represents the structural integrity of concrete dams and acts as a clear indicator of their operational performance. Predicting deformation is critical for monitoring the safety of hydraulic structures. To this end, this paper proposes an artificial intelligence-based process for predicting concrete dam deformation. Initially, using the principles of feature engineering, the preprocessing of deformation safety monitoring data is conducted. Subsequently, employing a stacking model fusion method, a novel prediction process embedded with multiple artificial intelligence algorithms is developed. Moreover, three new performance indicators—a superiority evaluation indicator, an accuracy evaluation indicator, and a generalization evaluation indicator—are introduced to provide a comprehensive assessment of the model’s effectiveness. Finally, an engineering example demonstrates that the ensemble artificial intelligence method proposed herein outperforms traditional statistical models and single machine learning models in both fitting and predictive accuracy, thereby providing a scientific and effective foundation for concrete dam deformation prediction and safety monitoring. Full article
(This article belongs to the Special Issue Remote Sensing, Artificial Intelligence and Deep Learning in Hydraulic Structure Safety Monitoring)
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23 pages, 5528 KiB  
Article
Settlement Prediction for Concrete Face Rockfill Dams Considering Major Factor Mining Based on the HHO-VMD-LSTM-SVR Model
by Xueqin Zheng, Taozhe Ren, Fengying Lv, Yu Wang and Sen Zheng
Water 2024, 16(12), 1643; https://doi.org/10.3390/w16121643 - 8 Jun 2024
Viewed by 1120
Abstract
Some important discoveries have been revealed in some studies, including that the settlement of concrete face rockfill dams (CFRDs) may cause cracks in the concrete face slabs, which may lead to dam collapse. Therefore, deformation behavior prediction of CFRDs is a longstanding and [...] Read more.
Some important discoveries have been revealed in some studies, including that the settlement of concrete face rockfill dams (CFRDs) may cause cracks in the concrete face slabs, which may lead to dam collapse. Therefore, deformation behavior prediction of CFRDs is a longstanding and emerging aspect of dam safety monitoring. This paper aims to propose a settlement prediction model for CFRDs combining the variational mode decomposition (VMD) algorithm, long short-term memory (LSTM) network, and support vector regression algorithm (SVR). Firstly, VMD is applied in the decomposition of dam settlement monitoring data to reduce its complexity. Furthermore, feature information on settlement time series is extracted. Secondly, the LSTM and SVR are optimized by the Harris hawks optimization (HHO) algorithm and modified least square (PLS) method to mine the major influencing factors and establish the prediction model with higher precision. Finally, the proposed model and other models are applied to predict the deformation behavior of the Yixing CFRD. Prediction results indicate that the proposed method possesses particular advantages over other models. The proposed VMD-LSTM-SVR model might help to evaluate the settlement trends and safety states of CFRDs. Full article
(This article belongs to the Special Issue Remote Sensing, Artificial Intelligence and Deep Learning in Hydraulic Structure Safety Monitoring)
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18 pages, 2773 KiB  
Article
Seepage–Deformation Coupling Analysis of a Core Wall Rockfill Dam Subject to Rapid Fluctuations in the Reservoir Water Level
by Xueqin Zheng, Bin Yan, Wei Wang, Kenan Du and Yixiang Fang
Water 2024, 16(11), 1621; https://doi.org/10.3390/w16111621 - 5 Jun 2024
Viewed by 1002
Abstract
Core wall rockfill dams are susceptible to cracking at the dam’s crest, as well as collapse and settlement of the rockfill during storage and operation periods, particularly due to rapid fluctuations in the water level in pumped storage power stations. Most studies on [...] Read more.
Core wall rockfill dams are susceptible to cracking at the dam’s crest, as well as collapse and settlement of the rockfill during storage and operation periods, particularly due to rapid fluctuations in the water level in pumped storage power stations. Most studies on the impact of fluctuations in the reservoir’s water level on dam deformation have considered fluctuations of less than 5 m/d, while pumped storage power stations experience much larger fluctuations. Additionally, the seepage and stress fields within the dam’s rock and soil interact and influence each other. Few studies have used the coupling theory of seepage and stress to analyze seepage and deformation in core wall rockfill dams. To address these issues, a finite element model using seepage–stress coupling theory was utilized to investigate the variations in the phreatic line, earth pressure, and deformation of a core wall rockfill dam due to rapid fluctuations in the reservoir’s water level. Additionally, the results of the finite element simulation were compared with and analyzed alongside safety monitoring data. The results indicated that, upon a sudden decrease in the reservoir’s water level, there was a lag in the decline of the phreatic line in Rockfill I, which created a large hydraulic gradient, resulting in a reverse seepage field on the dam’s slope surface and generating a drag force directed upstream. Consequently, a significant concentration of stress occurred on one-third of the upstream slope surface of the dam and the seepage curtain, and the increase in horizontal displacement was substantially greater than the increase in settlement from one-third of the rockfill’s height to the dam’s foundation. The deformation was more sensitive to the lowest water level of the reservoir rather than to the fastest rate of decline. Sudden rises in the reservoir’s water level result in decreased horizontal displacements and settlement of the dam. Amid rapid fluctuations of the reservoir’s water level, changes in the vertical earth pressure were more pronounced at the bottom of the core wall than in its midsection. Compared with the core wall, variations in the vertical earth pressure in the upstream and downstream filter layers were minor at similar elevations. A peak horizontal displacement of 6.5 mm was noted at one-third the height of Rockfill I, with the greatest increase in settlement of 3.5 mm at the dam’s crest. To ensure a project’s safety, it is crucial to control the elevation of the lowest point during a sudden drop in the reservoir’s level and to carefully monitor for cracks or voids within approximately one-third of the dam’s height in Rockfill I and at the dam crest. This study’s results provide a scientific basis for assessing core wall rockfill dams’ health and securing long-term safety at pumped storage power facilities. Full article
(This article belongs to the Special Issue Remote Sensing, Artificial Intelligence and Deep Learning in Hydraulic Structure Safety Monitoring)
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18 pages, 6352 KiB  
Article
Integrated Time-Dependent Analysis of a Hydraulic Structure on Soft Foundations during Construction
by Chao Xu, Liang Ye, Suli Pan and Wen Luo
Water 2024, 16(10), 1375; https://doi.org/10.3390/w16101375 - 11 May 2024
Viewed by 922
Abstract
An integrated model that considers multiphysics is necessary to accurately analyze the time-dependent response of hydraulic structures on soft foundations. This study develops an integrated superstructure–foundation–backfills model and investigates the time-dependent displacement and stress of a lock head project on a soft foundation [...] Read more.
An integrated model that considers multiphysics is necessary to accurately analyze the time-dependent response of hydraulic structures on soft foundations. This study develops an integrated superstructure–foundation–backfills model and investigates the time-dependent displacement and stress of a lock head project on a soft foundation during the construction period. Finite element analyses are conducted, incorporating a transient thermal creep model for concrete and an elasto-plastic consolidation model for the soil. The modified Cam-clay model is employed to describe the elasto-plastic behavior of the soil. Subsequently, global sensitivity analyses are conducted to determine the relative importance of the model parameters on the system’s response, using Garson’s and partial derivative algorithms based on the backpropagation (BP) neural network. The results indicate that the integrated system exhibits pronounced time-dependent displacement and stress, with dangerous values appearing during specific periods. These values are easily neglected, highlighting the importance of integrated time-dependent analysis. Construction activities, particularly the backfilling process, could cause a sudden change in stress and significantly impact the stress redistribution of the superstructure. Additionally, the mechanical properties of concrete have a significant impact on the stress on the superstructure, while the mechanical properties of the soil control the settlement of the integrated system. Full article
(This article belongs to the Special Issue Remote Sensing, Artificial Intelligence and Deep Learning in Hydraulic Structure Safety Monitoring)
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21 pages, 9411 KiB  
Article
Vison Transformer-Based Automatic Crack Detection on Dam Surface
by Jian Zhou, Guochuan Zhao and Yonglong Li
Water 2024, 16(10), 1348; https://doi.org/10.3390/w16101348 - 9 May 2024
Cited by 1 | Viewed by 1125
Abstract
Dam is an essential structure in hydraulic engineering, and its surface cracks pose significant threats to its integrity, impermeability, and durability. Automated crack detection methods based on computer vision offer substantial advantages over manual approaches with regard to efficiency, objectivity and precision. However, [...] Read more.
Dam is an essential structure in hydraulic engineering, and its surface cracks pose significant threats to its integrity, impermeability, and durability. Automated crack detection methods based on computer vision offer substantial advantages over manual approaches with regard to efficiency, objectivity and precision. However, current methods face challenges such as misidentification, discontinuity, and loss of details when analyzing real-world dam crack images. These images often exhibit characteristics such as low contrast, complex backgrounds, and diverse crack morphologies. To address the above challenges, this paper presents a pure Vision Transformer (ViT)-based dam crack segmentation network (DCST-net). The DCST-net utilizes an improved Swin Transformer (SwinT) block as the fundamental block for enhancing the long-range dependencies within a SegNet-like encoder–decoder structure. Additionally, we employ a weighted attention block to facilitate side fusion between the symmetric pair of encoder and decoder in each stage to sharpen the edge of crack. To demonstrate the superior performance of our proposed method, six semantic segmentation models have been trained and tested on both a self-built dam crack dataset and two publicly available datasets. Comparison results indicate that our proposed model outperforms the mainstream methods in terms of visualization and most evaluation metrics, highlighting its potential for practical application in dam safety inspection and maintenance. Full article
(This article belongs to the Special Issue Remote Sensing, Artificial Intelligence and Deep Learning in Hydraulic Structure Safety Monitoring)
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15 pages, 4481 KiB  
Article
A Deformation Analysis Method for Sluice Structure Based on Panel Data
by Zekai Ma, Benxing Lou, Zhenzhong Shen, Fuheng Ma, Xiang Luo, Wei Ye, Xing Li and Dongze Li
Water 2024, 16(9), 1287; https://doi.org/10.3390/w16091287 - 30 Apr 2024
Viewed by 1132
Abstract
Deformation, as the most intuitive index, can reflect the operation status of hydraulic structures comprehensively, and reasonable analysis of deformation behavior has important guiding significance for structural long-term service. Currently, the health evaluation of dam deformation behavior has attracted widespread attention and extensive [...] Read more.
Deformation, as the most intuitive index, can reflect the operation status of hydraulic structures comprehensively, and reasonable analysis of deformation behavior has important guiding significance for structural long-term service. Currently, the health evaluation of dam deformation behavior has attracted widespread attention and extensive research from scholars due to its great importance. However, given that the sluice is a low-head hydraulic structure, the consequences of its failure are easily overlooked without sufficient attention. While the influencing factors of the sluice’s deformation are almost identical to those of a concrete dam, nonuniform deformation is the key issue in the sluice’s case because of the uneven property of the external load and soil foundation, and referencing the traditional deformation statistical model of a concrete dam cannot directly represent the nonuniform deformation behavior of a sluice. In this paper, we assume that the deformation at various positions of the sluice consist of both overall and individual effects, where overall effect values describe the deformation response trend of the sluice structure under external loads, and individual effect values represent the degree to which the deformation of a single point deviates from the overall deformation. Then, the random coefficient model of panel data is introduced into the analysis of sluice deformation to handle the unobservable overall and individual effects. Furthermore, the maximum entropy principle is applied, both to approximate the probability distribution function of individual effect extreme values and to determine the early warning indicators, completing the assessment and analysis of the nonuniform deformation state. Finally, taking a project as an example, we show that the method proposed can effectively identify the overall deformation trend of the sluice and the deviation degree of each measuring point from the overall deformation, which provides a novel approach for sluice deformation behavior research. Full article
(This article belongs to the Special Issue Remote Sensing, Artificial Intelligence and Deep Learning in Hydraulic Structure Safety Monitoring)
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16 pages, 4069 KiB  
Article
The Forecast of Streamflow through Göksu Stream Using Machine Learning and Statistical Methods
by Mirac Nur Ciner, Mustafa Güler, Ersin Namlı, Mesut Samastı, Mesut Ulu, İsmail Bilal Peker and Sezar Gülbaz
Water 2024, 16(8), 1125; https://doi.org/10.3390/w16081125 - 15 Apr 2024
Cited by 1 | Viewed by 1257
Abstract
Forecasting streamflow in stream basin systems plays a crucial role in facilitating effective urban planning to mitigate floods. In addition to employing intricate hydrological modeling systems, machine learning and statistical techniques offer an alternative means for streamflow forecasts. Nonetheless, the precision and dependability [...] Read more.
Forecasting streamflow in stream basin systems plays a crucial role in facilitating effective urban planning to mitigate floods. In addition to employing intricate hydrological modeling systems, machine learning and statistical techniques offer an alternative means for streamflow forecasts. Nonetheless, the precision and dependability of these methods are subjects of paramount importance. This study rigorously investigates the effectiveness of three distinct machine learning techniques and two statistical approaches when applied to streamflow data from the Göksu Stream in the Marmara Region of Turkey, spanning from 1984 to 2022. Through a comparative analysis of these methodologies, this examination aims to contribute innovative advancements to the existing methodologies used in the prediction of streamflow data. The methodologies employed include machine learning methods such as Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Machine (SVM) and statistical methods such as Simple Exponential Smoothing (SES) and Autoregressive Integrated Moving Average (ARIMA) model. In the study, 444 data points between 1984 and 2020 were used as training data, and the remaining data points for the period 2021–2022 were used for streamflow forecasting in the test validation period. The results were evaluated using various metrics, such as the correlation coefficient (r), mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R2), and Nash–Sutcliffe efficiency (NSE). Upon analyzing the results, it was found that the model generated using the XGBoost algorithm outperformed other machine learning and statistical techniques. Consequently, the models implemented in this study demonstrate a high level of accuracy in predicting potential streamflow in the river basin system. Full article
(This article belongs to the Special Issue Remote Sensing, Artificial Intelligence and Deep Learning in Hydraulic Structure Safety Monitoring)
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19 pages, 7118 KiB  
Article
A Method for Identifying Gross Errors in Dam Monitoring Data
by Liqiu Chen, Chongshi Gu, Sen Zheng and Yanbo Wang
Water 2024, 16(7), 978; https://doi.org/10.3390/w16070978 - 28 Mar 2024
Cited by 2 | Viewed by 1221
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Remote Sensing, Artificial Intelligence and Deep Learning in Hydraulic Structure Safety Monitoring)
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19 pages, 3401 KiB  
Article
A Statistical Prediction Model for Sluice Seepage Based on MHHO-BiLSTM
by Zihui Huang, Chongshi Gu, Jianhe Peng, Yan Wu, Hao Gu, Chenfei Shao, Sen Zheng and Mingyuan Zhu
Water 2024, 16(2), 191; https://doi.org/10.3390/w16020191 - 5 Jan 2024
Cited by 2 | Viewed by 1480
Abstract
The current seepage prediction model of the sluice gate is rarely used. To solve the problem, this paper selects the bidirectional long and short-term neural network (BiLSTM) with high information integration and accuracy, which can well understand and capture the temporal pattern and [...] Read more.
The current seepage prediction model of the sluice gate is rarely used. To solve the problem, this paper selects the bidirectional long and short-term neural network (BiLSTM) with high information integration and accuracy, which can well understand and capture the temporal pattern and dependency relationship in the sequence and uses the multi-strategy improved Harris Hawks optimization algorithm (MHHO) to analyze its two hyperparameters: By optimizing the number of forward and backward neurons, the overfitting and long-term dependence problems of the neural network are solved, and the convergence rate is accelerated. Based on this, the MHHO-BiLSTM statistical prediction model of sluice seepage is established in this paper. To begin with, the prediction model uses water pressure, rainfall, and aging effects as input data. Afterward, the bidirectional long short-term memory neural network parameters are optimized using the multi-strategy improved Harris Hawks optimization algorithm. Then, the statistical prediction model based on the optimization algorithm proposed in this paper for sluice seepage is proposed. Finally, the seepage data of a sluice and its influencing factors are used for empirical analysis. The calculation and analysis results indicate that the optimization algorithm proposed in this paper can better search the optimal parameters of the bidirectional long short-term memory neural network compared with the original Harris Eagle optimization algorithm, optimizing the bidirectional long short-term memory neural network (HHO-BiLSTM) and the original bidirectional long short-term memory neural network (BiLSTM). Meanwhile, the bidirectional long and short-term neural network (BiLSTM) model shows higher prediction accuracy and robustness. Full article
(This article belongs to the Special Issue Remote Sensing, Artificial Intelligence and Deep Learning in Hydraulic Structure Safety Monitoring)
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18 pages, 5242 KiB  
Article
Research on Temperature Control Index for High Concrete Dams Based on Information Entropy and Cloud Model from the View of Spatial Field
by Guang Yang, Jin Sun, Jianwei Zhang, Jingtai Niu, Bowen Luan, Zhendong Huang and Ahui Zhao
Water 2023, 15(22), 4023; https://doi.org/10.3390/w15224023 - 20 Nov 2023
Cited by 2 | Viewed by 1223
Abstract
It is significant to adopt scientific temperature control criteria for high concrete dams in the construction period according to practical experience and theoretical calculation. This work synthetically uses information entropy and a cloud model and develops novel in situ observation data-based temperature control [...] Read more.
It is significant to adopt scientific temperature control criteria for high concrete dams in the construction period according to practical experience and theoretical calculation. This work synthetically uses information entropy and a cloud model and develops novel in situ observation data-based temperature control indexes from the view of a spatial field. The order degree and the disorder degree of observation values are defined according to the probability principle. Information entropy and weight parameters are combined to describe the distribution characteristics of the temperature field. Weight parameters are optimized via projection pursuit analysis (PPA), and then temperature field entropy (TFE) is constructed. Based on the above work, multi-level temperature control indexes are set up via a cloud model. Finally, a case study is conducted to verify the performance of the proposed method. According to the calculation results, the change law of TFEs agrees with actual situations, indicating that the established TFE is reasonable, the application conditions of the cloud model are wider than those of the typical small probability method, and the determined temperature control indexes improve the safety management level of high concrete dams. Research results offer scientific reference and technical support for temperature control standards adopted at other similar projects. Full article
(This article belongs to the Special Issue Remote Sensing, Artificial Intelligence and Deep Learning in Hydraulic Structure Safety Monitoring)
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16 pages, 3464 KiB  
Article
The Use of UAV for Measuring the Morphology of Ice Cover on the Surface of a River: A Case Study of the Low Head Dam and Fishway Inlet Area in the Odra River
by Jan Błotnicki, Paweł Jarzembowski, Maciej Gruszczyński and Marcin Popczyk
Water 2023, 15(22), 3972; https://doi.org/10.3390/w15223972 - 15 Nov 2023
Cited by 2 | Viewed by 1281
Abstract
The application of UAV to acquire data on the morphometry of frazil ice floe in motion is demonstrated in the measurements conducted in the area of the Wrocław Water Junction at the Opatowice weir on the Odra River (Poland). Image processing was performed [...] Read more.
The application of UAV to acquire data on the morphometry of frazil ice floe in motion is demonstrated in the measurements conducted in the area of the Wrocław Water Junction at the Opatowice weir on the Odra River (Poland). Image processing was performed using open-source software dedicated to image analysis. The methodology presented in the publication offers a cost-effective and low-overhead technique for describing ice phenomena in lowland rivers. The focus of the methodology was on measuring the area, average size, perimeter, and circularity of frazil ice floe. The measurements were carried out for individual frames captured by a UAV, and the results were analyzed using statistical techniques. In prior research, the team effectively assessed ice velocity on an identical test sample. Deriving the average velocity, surface area, and fundamental morphological traits of frazil ice facilitates the automated segmentation, classification, and prediction of potential risks related to ice blockages on water routes. These risks encompass potential waterway obstructions, as well as infrastructure impairments, and may pose a danger to human safety. Full article
(This article belongs to the Special Issue Remote Sensing, Artificial Intelligence and Deep Learning in Hydraulic Structure Safety Monitoring)
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20 pages, 5706 KiB  
Article
Prediction for the Sluice Deformation Based on SOA-LSTM-Weighted Markov Model
by Jianhe Peng, Wei Xie, Yan Wu, Xiaoran Sun, Chunlin Zhang, Hao Gu, Mingyuan Zhu and Sen Zheng
Water 2023, 15(21), 3724; https://doi.org/10.3390/w15213724 - 25 Oct 2023
Cited by 3 | Viewed by 1307
Abstract
Increasingly, deformation prediction has become an essential research topic in sluice safety control, which requires significant attention. However, there is still a lack of practical and efficient prediction modeling for sluice deformation. In order to address the limitations in mining the deep features [...] Read more.
Increasingly, deformation prediction has become an essential research topic in sluice safety control, which requires significant attention. However, there is still a lack of practical and efficient prediction modeling for sluice deformation. In order to address the limitations in mining the deep features of long-time data series of the traditional statistical model, in this paper, an improved long short-term memory (LSTM) model and weighted Markov model are introduced to predict sluice deformation. In the method, the seagull optimization algorithm (SOA) is utilized to optimize the hyper-parameters of the neural network structure in LSTM primarily to improve the model. Subsequently, the relevant error sequences of the fitting results of SOA-LSTM model are classified and the Markovity of the state sequence is examined. Then, the autocorrelation coefficients and weights of each order are calculated and the weighted and maximum probability values are applied to predict the future random state of the sluice deformation. Afterwards, the prediction model of sluice deformation on the SOA-LSTM-weighted Markov model is proposed. Ultimately, the presented model is used to predict the settlement characteristics of an actual sluice project in China. The analysis results demonstrate that the proposed model possesses the highest values of R2 and the smallest values of RMSE and absolute relative errors for the monitoring data of four monitoring points. Consequently, it concluded that the proposed method shows better prediction ability and accuracy than the SOA-LSTM model and the stepwise regression model. Full article
(This article belongs to the Special Issue Remote Sensing, Artificial Intelligence and Deep Learning in Hydraulic Structure Safety Monitoring)
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18 pages, 10230 KiB  
Article
An Improved ResNet-Based Algorithm for Crack Detection of Concrete Dams Using Dynamic Knowledge Distillation
by Jingying Zhang and Tengfei Bao
Water 2023, 15(15), 2839; https://doi.org/10.3390/w15152839 - 6 Aug 2023
Cited by 6 | Viewed by 1831
Abstract
Crack detection is an important component of dam safety monitoring. Detection methods based on deep convolutional neural networks (DCNNs) are widely used for their high efficiency and safety. Most existing DCNNs with high accuracy are too complex for users to deploy for real-time [...] Read more.
Crack detection is an important component of dam safety monitoring. Detection methods based on deep convolutional neural networks (DCNNs) are widely used for their high efficiency and safety. Most existing DCNNs with high accuracy are too complex for users to deploy for real-time detection. However, compressing models face the dilemma of sacrificing detection accuracy. To solve this dilemma, an improved residual neural network (ResNet)-based algorithm for concrete dam crack detection using dynamic knowledge distillation is proposed in this paper in order to obtain higher accuracy for small models. To see how well distillation works, preliminary experiments were carried out on mini-ImageNet. ResNet18 was trained by adding additional tasks to match soft targets generated by ResNet50 under dynamic high temperatures. Furthermore, these pre-trained teacher and student models were transferred to experiments on concrete crack detection. The results showed that the accuracy of the improved algorithm was up to 99.85%, an increase of 4.92%. Full article
(This article belongs to the Special Issue Remote Sensing, Artificial Intelligence and Deep Learning in Hydraulic Structure Safety Monitoring)
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16 pages, 5051 KiB  
Article
Stability Study of a Double-Row Steel Sheet Pile Cofferdam Structure on Soft Ground
by Yan Jiang, Fei Guo, Wenlong Wang, Guanghua Yang, Jinchao Yue and Yibin Huang
Water 2023, 15(14), 2643; https://doi.org/10.3390/w15142643 - 20 Jul 2023
Cited by 1 | Viewed by 5388
Abstract
The stability of a double-row steel sheet pile cofferdam structure under soft ground conditions was investigated in this study, using the temporary cofferdam of the Shenzhen–Zhongshan cross-river channel as the engineering background. The stability of the cofferdam design solution was calculated with a [...] Read more.
The stability of a double-row steel sheet pile cofferdam structure under soft ground conditions was investigated in this study, using the temporary cofferdam of the Shenzhen–Zhongshan cross-river channel as the engineering background. The stability of the cofferdam design solution was calculated with a model that incorporates factors such as the coordination of independent pile top displacement, as well as the m-value for backfilled sand and the thrown rock body. The internal force and displacement results of the cofferdam under different working conditions are obtained. And the entire construction process was analyzed using the finite element method. The results indicate that the overall stability and overturning stability of the cofferdam satisfy relevant safety requirements, with minimum safety factors of 1.744 and 1.400, respectively. The maximum displacement of the inner and outer steel sheet piles is 34 mm, the maximum bending moment is 249.30 kN·m, and the maximum shear force is 266.66 kN. The displacements of sheet piles were within an acceptable range, and the internal forces remained below the load capacity of the selected sheet pile type for the design. Based on these findings, the cofferdam structure can be considered safe and satisfying the specified requirements. This work may have instructive value for cofferdam design and construction. Full article
(This article belongs to the Special Issue Remote Sensing, Artificial Intelligence and Deep Learning in Hydraulic Structure Safety Monitoring)
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Review

Jump to: Editorial, Research

28 pages, 16218 KiB  
Review
A Review of Stability of Dam Structures in Coal Mine Underground Reservoirs
by Yan Wang, Fei Liu, Miaomiao Kou and Mingfei Li
Water 2024, 16(13), 1856; https://doi.org/10.3390/w16131856 - 28 Jun 2024
Cited by 1 | Viewed by 868
Abstract
Coal has remained the primary component of China’s energy structure, and high-intensity extraction has continued in the central and western coal-producing regions of China. In contrast to the abundant coal resources, water resources have become extremely scarce in these regions, creating a conflict [...] Read more.
Coal has remained the primary component of China’s energy structure, and high-intensity extraction has continued in the central and western coal-producing regions of China. In contrast to the abundant coal resources, water resources have become extremely scarce in these regions, creating a conflict between coal resource extraction and water resource conservation. The coal mine underground reservoir (CMUR), as a typical technology for combined coal and water extraction and water-preserving coal mining, has been applied in numerous mines in central and western China. This effectively alleviates water resource shortages and achieves the goal of water resource conservation. The CMURs utilizes the goaf created by longwall mining as the water storage space. The reservoir dam structure comprises coal pillars, which serve as protective coal pillars in the mining area, and artificial dam structures that filled the gaps between these coal pillars. The stability of the dam structure under the complex stress effects of hydraulic coupling has been identified as the key to maintaining the safe operation of the CMUR. The mechanical properties, stress field, fracture field, and seepage field (“three fields”) change mechanisms, as well as the research results on size optimization of coal pillar dams and artificial dams in CMURs, were systematically reviewed. The core content included the instability and failure mechanisms of dam structures under the comprehensive coupling effects of factors such as dry–wet cycles of mine water, long-term immersion, chemical effects of high-salinity water, dynamic and static loads, and cyclic loads. This paper is considered to have certain reference value for the study of the stability of dam structures in CMURs and to provide some guidance for the safe operation of CMURs. Full article
(This article belongs to the Special Issue Remote Sensing, Artificial Intelligence and Deep Learning in Hydraulic Structure Safety Monitoring)
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