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Application of Artificial Intelligence in Hydraulic Engineering
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Application of Artificial Intelligence in Hydraulic Engineering

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 (31 December 2023) | Viewed by 23783

Special Issue Editors

Prof. Dr. Jie Yang

E-Mail Website
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
Dr. Chunhui Ma

E-Mail Website
Guest Editor
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
Dr. Lin Cheng

E-Mail Website
Guest Editor
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

Special Issue Information

Dear Colleagues,

The intelligent algorithm has become an important research method to solve critical scientific problems in the engineering field. It has been widely used in the optimal design, structural simulation, safety monitoring and safety evaluation of water conservancy projects due to its advantages in regression, classification, clustering and dimension reduction. Experiments and numerical simulations are faced with constraints of time and cost in traditional research methods. With the advancement of sensors and measurement technology, a large amount of safety-monitoring data has been accumulated in water-conservancy projects. Intelligent algorithms have become a powerful tool for monitoring data, mining information and constructing data associations quickly and accurately. Combined with traditional computing techniques such as geotechnical tests, non-destructive testing and numerical simulation, intelligent algorithms will help us further understand various laws and mechanisms in water-conservancy projects, which is of great significance to improving the safety of water-conservancy projects and the development level of human society. Therefore, this special theme will focus on applying intelligent algorithms in water conservancy projects. We would like to invite you to submit your research papers to this particular issue. Suitable topics include but are not limited to the following: data analysis of dam monitoring, inverse analysis of material parameters, agent model of the numerical simulation method, safety evaluation of hydraulic structures and various intelligent models of water-conservancy projects.

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

  • artificial Intelligence
  • hydraulic engineering
  • safety monitoring
  • data analysis
  • numerical simulation

Published Papers (16 papers)

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Editorial

Jump to: Research

5 pages, 154 KiB  
Editorial
Application of Artificial Intelligence in Hydraulic Engineering
by Chunhui Ma, Lin Cheng and Jie Yang
Water 2024, 16(4), 590; https://doi.org/10.3390/w16040590 - 17 Feb 2024
Viewed by 1194
Abstract
Water conservancy projects have always been essential throughout the development of human society, including the development and utilization of water resources, the construction and management of water conservancy facilities and flood prevention and control [...] Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Hydraulic Engineering)

Research

Jump to: Editorial

17 pages, 12877 KiB  
Article
A Combined Noise Reduction Method for Floodgate Vibration Signals Based on Adaptive Singular Value Decomposition and Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise
by Wentao Wang, Huiqi Zhu, Yingxin Cheng, Yiyuan Tang, Bo Liu, Huokun Li, Fan Yang, Wenyuan Zhang, Wei Huang and Fang Zheng
Water 2023, 15(24), 4287; https://doi.org/10.3390/w15244287 - 15 Dec 2023
Viewed by 944
Abstract
To address the issue of the vibration characteristic signals of floodgates being affected by background white noise and low-frequency water flow noise, a noise reduction method combining the improved adaptive singular value decomposition algorithm (ASVD) and the improved complete ensemble EMD with adaptive [...] Read more.
To address the issue of the vibration characteristic signals of floodgates being affected by background white noise and low-frequency water flow noise, a noise reduction method combining the improved adaptive singular value decomposition algorithm (ASVD) and the improved complete ensemble EMD with adaptive noise (ICEEMDAN) is proposed. Firstly, a Hankel matrix is constructed based on the collected discrete time signals. After performing SVD on the Hankel matrix, the ASVD algorithm is used to automatically select the effective singular values to filter out most of the background white noise and retain the useful frequency components with similar energy in the signal. Then, ICEEMDAN combined with the Spearman correlation coefficient method is used to further filter out residual white noise and low-frequency water flows. The noise reduction performance of this combined method is verified through simulation experiments. Filtered by the ASVD-ICEEMDAN method, the signal-to-noise ratio of the simulation signal (50% noise level) is increased from 4.417 to 16.237, and the root mean square error is reduced from 2.286 to 0.586. Based on the practically measured vibration signals of a floodgate at a large hydropower station, the result shows that the ASVD-ICEEMDAN method exhibits good noise reduction performance and feature information extraction abilities for floodgate vibration signals, and can provide support for operational mode analysis and damage identification of practical structures under complex interference conditions. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Hydraulic Engineering)
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23 pages, 6128 KiB  
Article
Deep-Learning-Enhanced CT Image Analysis for Predicting Hydraulic Conductivity of Coarse-Grained Soils
by Jiayi Peng, Zhenzhong Shen, Wenbing Zhang and Wen Song
Water 2023, 15(14), 2623; https://doi.org/10.3390/w15142623 - 19 Jul 2023
Cited by 1 | Viewed by 1581
Abstract
Permeability characteristics in coarse-grained soil is pivotal for enhancing the understanding of its seepage behavior and effectively managing it, directly impacting the design, construction, and operational safety of embankment dams. Furthermore, these insights bridge diverse disciplines, including hydrogeology, civil engineering, and environmental science, [...] Read more.
Permeability characteristics in coarse-grained soil is pivotal for enhancing the understanding of its seepage behavior and effectively managing it, directly impacting the design, construction, and operational safety of embankment dams. Furthermore, these insights bridge diverse disciplines, including hydrogeology, civil engineering, and environmental science, broadening their application and relevance. In this novel research, we leverage a Convolutional Neural Network (CNN) model to achieve the accurate segmentation of coarse-grained soil CT images, surpassing traditional methods in precision and opening new avenues in soil granulometric analysis. The three-dimensional (3D) models reconstructed from the segmented images attest to the effectiveness of our CNN model, highlighting its potential for automation and precision in soil-particle analysis. Our study uncovers and validates new empirical formulae for the ideal particle size and the discount factor in coarse-grained soils. The robust linear correlation underlying these formulae deepens our understanding of soil granulometric characteristics and predicts their hydraulic behavior under varying gradients. This advancement holds immense value for soil-related engineering and hydraulic applications. Furthermore, our findings underscore the significant influence of granular composition, particularly the concentration of fine particles, on the tortuosity of water-flow paths and the discount factor. The practical implications extend to multiple fields, including water conservancy and geotechnical engineering. Altogether, our research represents a significant step in soil hydrodynamics research, where the CNN model’s application unveils key insights into soil granulometry and hydraulic conductivity, laying a strong foundation for future research and applications. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Hydraulic Engineering)
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24 pages, 4965 KiB  
Article
Study on the Evolution of Risk Contagion in Urban River Ecological Management Projects Based on SEIRS
by Junke Xu, Jiwei Zhu and Jiancang Xie
Water 2023, 15(14), 2622; https://doi.org/10.3390/w15142622 - 19 Jul 2023
Cited by 1 | Viewed by 1105
Abstract
The risk transmission mechanisms of urban river ecological management engineering projects are examined in this study. Using the Susceptible Exposed Infectious Recovered Susceptible (SEIRS) model for risk transmission, a model of risk propagation delay for urban river ecological management engineering projects on scale-free [...] Read more.
The risk transmission mechanisms of urban river ecological management engineering projects are examined in this study. Using the Susceptible Exposed Infectious Recovered Susceptible (SEIRS) model for risk transmission, a model of risk propagation delay for urban river ecological management engineering projects on scale-free networks is developed, which takes into account the effects of risk propagation and delay. We conducted a steady-state analysis of the model and obtained the basic reproduction number R. When R > 1, the equilibrium point of risk outbreak is stable, and when R < 1, the equilibrium point of risk disappearance is stable. Numerical simulations of the model were conducted using the MATLAB2022b to reveal the dynamic propagation patterns of risk in urban river ecological management engineering projects. The research results show that the steady-state density of the infected nodes in the network increases with the increase in the effective propagation rate and the propagation delay time; the propagation delay reduces the risk propagation threshold in the network and accelerates the occurrence of the equilibrium state of risk outbreak. There is a correlation between the transmission rate of latent nodes and the transmission rate of infected nodes, and the effective transmission rate of latent nodes has a greater influence on risk propagation. The spread of risk in the network can be effectively controlled and mitigated with targeted immunity for susceptible nodes. This article, based on the theory of complex networks and the mean-field theory, takes into account the propagation delay and spreading of latent nodes. Building a D-SEIRS model for risk propagation broadens the research perspective on urban river ecological management risk propagation. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Hydraulic Engineering)
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14 pages, 8483 KiB  
Article
Dynamic Forecasting and Operation Mechanism of Reservoir Considering Multi-Time Scales
by Chengyu Han, Zhen Guo, Xiaomei Sun and Yuquan Zhang
Water 2023, 15(13), 2472; https://doi.org/10.3390/w15132472 - 5 Jul 2023
Viewed by 1091
Abstract
This paper proposes a feedback, rolling and adaptive operation decision-making mechanism for coupling and nesting of time scales. It is aimed at the change of time scale and the dynamics in the operation process, considering the relationship between operation period and multi-time scales. [...] Read more.
This paper proposes a feedback, rolling and adaptive operation decision-making mechanism for coupling and nesting of time scales. It is aimed at the change of time scale and the dynamics in the operation process, considering the relationship between operation period and multi-time scales. The key point is to integrate forecasting and operation in order to adapt to the multi-time scales dynamic change in the operation process. The operation process is divided into different time scales; forecasting and operation model method libraries are constructed, and the progressive updating and nesting mechanism are used to realize the process dynamic operation, according to the regulation period or operation period of the reservoir. Taking the Miyun Reservoir in Beijing, China as the research object, the operation mechanism is integrated into the operation process, and the complex forecasting operation and control mechanism are integrated, based on the integrated platform and using modern information technology. The forecasting and operation method uses classic different models, which can be selected based on different goals. The forecasting inflow is used as input, and the output is the water distribution plan, more importantly, the mechanism in the operation process is the key point. This is a rolling modification of the inflow process in the next stage, and the operation plan also changes accordingly. The feasibility, effectiveness, rationality and flexibility of the reservoir dynamic and adaptive operation are verified, so that the reservoir operation is dynamically changing and adapting to the changing demand. The proposed operation mechanism has scientific value and guiding significance to improve the reservoir operation theory, and it provides decision support for the actual reservoir operation and operation business. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Hydraulic Engineering)
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14 pages, 1802 KiB  
Article
Comprehensive Evaluation Method for the Safety State of RCC Dams Based on Interval Number Theory
by Xudong Chen, Peng Xu, Xinyi Liu and Chen Su
Water 2023, 15(11), 2089; https://doi.org/10.3390/w15112089 - 31 May 2023
Viewed by 1180
Abstract
Roller Compacted Concrete (RCC) dams are critical infrastructure, playing an important role in economic and social development. However, dam failure can cause great losses. To mitigate hazards, studies of methods to deal with the uncertainty involved in the comprehensive evaluation process of the [...] Read more.
Roller Compacted Concrete (RCC) dams are critical infrastructure, playing an important role in economic and social development. However, dam failure can cause great losses. To mitigate hazards, studies of methods to deal with the uncertainty involved in the comprehensive evaluation process of the safety state of RCC dams are hot issues. Interval number theory is applied to quantify the uncertainty in this study. A comprehensive evaluation indicator system is explored, an approach to allocating the indicator weight rationally is proposed, and a comprehensive evaluation model is established. Comprehensive evaluation standards are developed. An RCC dam in China is used to illustrate the applicability of this comprehensive evaluation method based on interval number theory. The results indicate that the method and models proposed are suitable for comprehensively evaluating the safety state of RCC dams and can be used as a new procedure to monitor the safety of an RCC dam. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Hydraulic Engineering)
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21 pages, 3185 KiB  
Article
Intelligent Detection Method for Concrete Dam Surface Cracks Based on Two-Stage Transfer Learning
by Jianyuan Li, Xiaochun Lu, Ping Zhang and Qingquan Li
Water 2023, 15(11), 2082; https://doi.org/10.3390/w15112082 - 31 May 2023
Cited by 2 | Viewed by 1637
Abstract
The timely identification and detection of surface cracks in concrete dams, an important public safety infrastructure, is of great significance in predicting engineering hazards and ensuring dam safety. Due to their low efficiency and accuracy, manual detection methods are gradually being replaced by [...] Read more.
The timely identification and detection of surface cracks in concrete dams, an important public safety infrastructure, is of great significance in predicting engineering hazards and ensuring dam safety. Due to their low efficiency and accuracy, manual detection methods are gradually being replaced by computer vision techniques, and deep learning semantic segmentation methods have higher accuracy and robustness than traditional image methods. However, the lack of data images and insufficient detection performance remain challenges in concrete dam surface crack detection scenarios. Therefore, this paper proposes an intelligent detection method for concrete dam surface cracks based on two-stage transfer learning. First, relevant domain knowledge is transferred to the target domain using two-stage transfer learning, cross-domain and intradomain learning, allowing the model to be fully trained with a small dataset. Second, the segmentation capability is enhanced by using residual network 50 (ResNet50) as a UNet model feature extraction network to enhance crack feature information extraction. Finally, multilayer parallel residual attention (MPR) is integrated into its jump connection path to improve the focus on critical information for clearer fracture edge segmentation. The results show that the proposed method achieves optimal mIoU and mPA of 88.3% and 92.7%, respectively, among many advanced semantic segmentation models. Compared with the benchmark UNet model, the proposed method improves mIoU and mPA by 4.6% and 3.2%, respectively, reduces FLOPs by 36.7%, improves inference speed by 48.9%, verifies its better segmentation performance on dam face crack images with a low fine crack miss detection rate and clear crack edge segmentation, and achieves an accuracy of over 85.7% in crack area prediction. In summary, the proposed method has higher efficiency and accuracy in concrete dam face crack detection, with greater robustness, and can provide a better alternative or complementary approach to dam safety inspections than the benchmark UNet model. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Hydraulic Engineering)
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19 pages, 1762 KiB  
Article
Research on Risk Evolution Mechanism of Urban River Ecological Governance Project Based on Social Network Analysis
by Junke Xu, Jiwei Zhu and Jiancang Xie
Water 2023, 15(11), 2012; https://doi.org/10.3390/w15112012 - 25 May 2023
Cited by 1 | Viewed by 1053
Abstract
The evolution and transfer of risk elements of urban river ecological management projects are primarily responsible for the difficulty of risk management in these projects. In this paper, we identify 63 risk elements of urban river ecological management projects using in-depth literature reviews [...] Read more.
The evolution and transfer of risk elements of urban river ecological management projects are primarily responsible for the difficulty of risk management in these projects. In this paper, we identify 63 risk elements of urban river ecological management projects using in-depth literature reviews and brainstorming. The association among all the risk elements is constructed using an expert survey method, and the risk elements are utilized as network nodes. The relationships between these nodes are then used as network edges (i.e., paths) to construct a complex network model. By using the network visualization and analysis tool anaconda3, we analyze the overall and local characteristic parameters of the risk network. The risk transmission characteristics of the urban river ecological management project are analyzed according to the parameter characteristics to reveal the inner relationships of risk transmission inherent in the complex network. We use the Jinghe ecological management project in Jinghe New City to verify the effectiveness of the proposed model. The study demonstrates that the starting node risk needs to be controlled, and the conduction node that indirectly triggers risk propagation needs to be cut off to achieve risk prevention and control. Accordingly, the risk prevention strategy is proposed, namely, paying close attention to the starting nodes of schedule delay risk, construction cycle risk and cost overrun risk, as well as the conduction risk nodes of project complexity risk, quality assessment risk, construction accident risk and improper drawing design risk. Effective measures should be taken to control the transmission and occurrence of risks based on these two aspects. The study reveals the network evolution of risk factors, which enriches the theory of the risk factor network evolution and evaluation of urban river ecological management projects. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Hydraulic Engineering)
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20 pages, 19624 KiB  
Article
Analysis of the Hydromechanical Properties of Compact Sandstone and Engineering Application
by Peng Tang, Wenbing Zhang, Haoyu Wang, Jiaxin Zhou, Yabin Dang and Zhiming Chao
Water 2023, 15(11), 2011; https://doi.org/10.3390/w15112011 - 25 May 2023
Cited by 1 | Viewed by 1214
Abstract
The paper proposes a method to simulate the mechanical behavior of compact rock considering hydromechanics by combining physical experiments and numerical analysis. The effectiveness of the constructed method is validated by the comparison between the numerical and physical results of triaxial shear experiments [...] Read more.
The paper proposes a method to simulate the mechanical behavior of compact rock considering hydromechanics by combining physical experiments and numerical analysis. The effectiveness of the constructed method is validated by the comparison between the numerical and physical results of triaxial shear experiments on sandstone in seepage conditions. Based on the validated method, the stability of underground water-sealed oil and gas storage caverns in surrounding compact sandstone during excavation is analyzed. The main findings are as follows: The intrinsic permeability of compact sandstone has a power function relationship with the porosity; the combination of the porous media elastic model and the modified Drucker–Prager plasticity model can preciously represent the mechanical properties of compact sandstone; the proposed method can accurately replicate the hydromechanical response of compact sandstone in seepage conditions; the effects of hydromechanical effects have significant impacts on the stability of surround compact sandstone during the excavation of underground water sealed oil and gas storage caverns, which causes the obvious increase in stress, deformation and plastic deformation zones of the surrounding compact sandstone and remarkable decrease in the stability safety factor. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Hydraulic Engineering)
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17 pages, 5555 KiB  
Article
Intelligent Inversion Analysis of Hydraulic Engineering Geological Permeability Coefficient Based on an RF–HHO Model
by Wei Zhao, Qiaogang Yin and Lifeng Wen
Water 2023, 15(11), 1993; https://doi.org/10.3390/w15111993 - 24 May 2023
Viewed by 994
Abstract
The permeability of the natural geology plays a crucial role in accurately analyzing seepage behavior in the project area. This study presents a novel approach for the inverse analysis of the permeability coefficient. The finite element model (FEM) combined with orthogonal experimental design [...] Read more.
The permeability of the natural geology plays a crucial role in accurately analyzing seepage behavior in the project area. This study presents a novel approach for the inverse analysis of the permeability coefficient. The finite element model (FEM) combined with orthogonal experimental design is used to construct a sample set of permeability coefficient inversion. The established random forest (RF) algorithm surrogate model is applied to determine the optimal values of permeability parameters in the project area using the Harris hawk optimization (HHO) algorithm. This method was used to explore and verify the distribution of natural seepage fields for the P hydropower station. The results showed that the RF model outperformed the classical CART and BP models at each borehole regarding performance evaluation indices. Furthermore, the water head prediction results were more accurate, and the RF model performed admirably in terms of prediction, anti-interference, and generalization. The HHO algorithm effectively searched for the optimal permeability coefficient of the geology. The maximum value of the relative error of the borehole water head inverted was 1.11%, and the accuracy met engineering standards. The initial seepage field distribution pattern calculated followed the basic distribution pattern of the mountain seepage field. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Hydraulic Engineering)
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17 pages, 8307 KiB  
Article
Parameter Optimization of Centrifugal Pump Splitter Blades with Artificial Fish Swarm Algorithm
by Qidi Ke, Lingfeng Tang, Wenbin Luo and Jingzhe Cao
Water 2023, 15(10), 1806; https://doi.org/10.3390/w15101806 - 9 May 2023
Cited by 1 | Viewed by 2042
Abstract
Low specific speed centrifugal pumps typically suffer from low efficiency and severe backflow; adding optimally structured splitter blades can play a role. In this paper, the distribution of pressure and velocity in the flow channel is analyzed using CFD simulation for a low [...] Read more.
Low specific speed centrifugal pumps typically suffer from low efficiency and severe backflow; adding optimally structured splitter blades can play a role. In this paper, the distribution of pressure and velocity in the flow channel is analyzed using CFD simulation for a low specific speed centrifugal pump. The geometric parameters of the splitter blade are optimized using an orthogonal test and an artificial fish swarm algorithm; then the optimal splitter blade structure is obtained. Results showed that the splitter blade not only effectively solves the backflow of the flow channel and compresses the range of the trailing vortex, but it also alleviates the cavitation at the inlet of the main blade. When considering the best head, the order of influence of each factor is: Splitter blade thickness > Splitter blade inlet diameter > Splitter blade inlet width. At this time, the thickness of the splitter blade is 4.5 mm, splitter blade inlet diameter is 155 mm (0.775) and Splitter blade inlet width is 23 mm. Through the closed pump experimental system, it is confirmed that hydraulic performance has been improved. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Hydraulic Engineering)
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21 pages, 4834 KiB  
Article
Application of Artificial Neural Networks for Predicting Small Urban-Reservoir Volumes: The Case of Torregrotta Town (Italy)
by Biagio Saya and Carla Faraci
Water 2023, 15(9), 1747; https://doi.org/10.3390/w15091747 - 1 May 2023
Cited by 1 | Viewed by 1367
Abstract
In the hydraulic construction field, approximated formulations have been widely used for calculating tank volumes. Identifying the proper water reservoir volumes is of crucial importance in order to not only satisfy water demand but also to avoid unnecessary waste in the construction phase. [...] Read more.
In the hydraulic construction field, approximated formulations have been widely used for calculating tank volumes. Identifying the proper water reservoir volumes is of crucial importance in order to not only satisfy water demand but also to avoid unnecessary waste in the construction phase. In this perspective, the planning and management of small reservoirs may have a positive impact on their spatial distribution and storage capacities. The purpose of this study is, therefore, to suggest an alternative approach to estimate the optimal volume of small urban reservoirs. In particular, an artificial neural network (ANN) is proposed to predict future water consumption as a function of certain environmental parameters, such as rainy days, temperature and the number of inhabitants. As the water demand is strongly influenced by such quantities, their future trend is recovered by means of the Copernicus Climate Change Service (C3S) over the next 10 years. Finally, based on ANN prediction of the future consumption requirements, the continuity equation applied to tanks was resolved through integral-discretization obtaining the time-series volume variation and the total number of crisis events. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Hydraulic Engineering)
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19 pages, 10758 KiB  
Article
Laboratory Model Test and Field In Situ Test of Distributed Optical Fiber Monitoring of Seepage in a Karst Depression Reservoir Basin
by Bo Yu, Chunyong Shen, Hao Peng and Fawang Guo
Water 2023, 15(8), 1477; https://doi.org/10.3390/w15081477 - 10 Apr 2023
Viewed by 1055
Abstract
Karst depressions are ideal places for building reservoirs and stacking wastes, but karst depressions are mostly located in areas with strong karst development, and there is a problem of karst leakage. The study of karst depression monitoring methods can provide technical support for [...] Read more.
Karst depressions are ideal places for building reservoirs and stacking wastes, but karst depressions are mostly located in areas with strong karst development, and there is a problem of karst leakage. The study of karst depression monitoring methods can provide technical support for the construction of reservoirs in karst depressions, and leakage monitoring technology based on distributed temperature sensing (DTS) has the clear advantages of a large measurement range, high precision, continuity, wide distribution, large area, etc. In this paper, a temperature-sensing optical cable is first used to conduct leakage monitoring tests in different soil media, and the temperature change curve in different media is obtained, which verifies the feasibility of the heatable temperature-sensing optical cable to identify leakage in soil media with different moisture contents. Then, for the heatable distributed temperature-sensing optical cable, the variation law of the temperature eigenvalues of the sensing optical cable under different seepage velocities is studied using the layout method wrapped with saturated medium-coarse sand, and the relationship between the seepage rate and the temperature eigenvalues was analyzed. A regression formula for quantitative analysis of leakage velocity was established; finally, the manganese slag silo project in Songtao County in Guizhou Province, China, was used to simulate different leakage conditions on site for testing, and the data were compared and analyzed to obtain the route under different conditions. The temperature distribution law of the seepage section verifies the feasibility of the application of heatable temperature-sensing optical cables in the seepage monitoring of karst depressions. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Hydraulic Engineering)
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16 pages, 2593 KiB  
Article
A Prediction Model and Factor Importance Analysis of Multiple Measuring Points for Concrete Face Rockfill Dam during the Operation Period
by Lei Shao, Ting Wang, Youde Wang, Zilong Wang and Kaiyi Min
Water 2023, 15(6), 1081; https://doi.org/10.3390/w15061081 - 11 Mar 2023
Cited by 4 | Viewed by 1564
Abstract
Dam settlement monitoring is a crucial project in the safety management of concrete face rockfill dams (CFRD) over their whole life cycle. With the development of an automatic monitoring system, a large amount of settlement data was collected. To precisely predict the structural [...] Read more.
Dam settlement monitoring is a crucial project in the safety management of concrete face rockfill dams (CFRD) over their whole life cycle. With the development of an automatic monitoring system, a large amount of settlement data was collected. To precisely predict the structural health of dams, a combined multiple monitoring points (MMP) model and a machine learning model has been developed. In this paper, based on the physical factors of the CFRD, we comprehensively analyzed the influence of water level load transfer, rockfill rheology and soil properties on the settlement during the impoundment operation period. Then, we established a space-time distribution model of the CFRD during its operation period under multiple factors. An extreme gradient boosting (XGBoost) model was used for fitting prediction, and the model was evaluated using various performance indicators. The results show that spatial parameters such as the upper filling height, rockfill thickness, panel-point distance and soil material correlate to the deformation characteristics of the rockfill dam. Taking the monitoring data of the settlement of the Liyuan CFRD as an example, the new MMP model was evaluated and used to predict the settlement of the full-section points with higher accuracy, which has certain application and popularization value for related projects. Then, to evaluate the contribution of the components of the new MMP model, the SHapley Additive explanation (SHAP) methods are used to evaluate the importance of the selected factors, and the reasonability of these factors is verified. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Hydraulic Engineering)
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25 pages, 8131 KiB  
Article
Hydrometer Design Based on Thin-Film Resistive Sensor for Water Measurement in Residential Buildings
by Laís dos S. Gonçalves, Khrissy A. R. Medeiros and Carlos R. Hall Barbosa
Water 2023, 15(6), 1045; https://doi.org/10.3390/w15061045 - 9 Mar 2023
Cited by 2 | Viewed by 2017
Abstract
Because of economic, population, and consumption patterns changes, the use of freshwater has increased significantly in the last 100 years. Notably, measurement is essential to encourage water conservation. Thus, the present study aims to evaluate the applicability of a thin-film resistive sensor (bend [...] Read more.
Because of economic, population, and consumption patterns changes, the use of freshwater has increased significantly in the last 100 years. Notably, measurement is essential to encourage water conservation. Thus, the present study aims to evaluate the applicability of a thin-film resistive sensor (bend sensor) with different coatings for implementation in individualized water measurement systems. The motivation of this work is to propose a volumetric meter using flow control valves that ordinarily are already present in a building’s hydraulic installations. Methodologically, the following are presented: the system developed for the electromechanical and thermal characterization of the sensor, the sensor computational simulation performed using Ansys® software, and for the electronic circuit designed in LTSpice® software, the artificial neural network used to estimate the flow and the volume estimates from the trapezoidal pulses. The results obtained allowed us to assess that, taking into account the type of coating, the sensor coated with polyester has better behavior for the proposed hydrometer. In addition, this evaluation allowed us to conclude that the bend sensor demonstrated its feasibility to be used as a transducer of this novel type of volumetric meter and can be easily inserted inside a hydraulic component, such as a flow control valve, for example. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Hydraulic Engineering)
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25 pages, 5767 KiB  
Article
Numerical Simulation Study on the Influence of Construction Load on the Cutoff Wall in Reservoir Engineering
by Yongshuai Sun, Anping Lei, Ke Yang and Guihe Wang
Water 2023, 15(5), 993; https://doi.org/10.3390/w15050993 - 5 Mar 2023
Cited by 1 | Viewed by 1430
Abstract
Relying on the Beijing-Shijiazhuang Expressway widening project near the impervious wall of a reservoir, this paper uses FLAC3D two-dimensional and three-dimensional numerical simulation methods to establish the whole process model of the impervious wall of the reservoir affected by the construction load of [...] Read more.
Relying on the Beijing-Shijiazhuang Expressway widening project near the impervious wall of a reservoir, this paper uses FLAC3D two-dimensional and three-dimensional numerical simulation methods to establish the whole process model of the impervious wall of the reservoir affected by the construction load of the high-way reconstruction section. The stress and strain state of the cut-off wall in the high-way reconstruction section and the nearby reservoir is simulated in detail, the overall deformation of the cut-off wall in the reservoir is directly reflected, and the interaction and differential deformation between the wall structures are reflected. The safety and stability of the cutoff wall of the reservoir affected by the construction load are evaluated so that various advanced mechanical behaviors of the cutoff wall can be predicted. Research results show that the horizontal displacement value of the wall gradually increases from bottom to top, and the maximum value appears at the top of the wall. The horizontal displacement value of the 1–3 walls is relatively large, with the maximum value of 22.368 mm, and the horizontal displacement value of the 4–10 walls shows little difference. This is on account of the gravity of the backfill, the strata in the whole project area having settled, and the settlement at the bottom of the cut-off wall being 2.542 mm. At the root of the rigid cut-off wall, the compressive stress concentration occurs, with the maximum value between 1.75 MPa and 2.15 MPa. Due to the size of the structure, the maximum tensile stress of 0.237 MPa appears in the local area near the guide wall of the rigid cut-off wall, which will not endanger the rigid cut-off wall because of its small value. The maximum stress in the rigid impervious wall and the plastic impervious wall are 1.90–2.15 MPa and 1.00–1.12 MPa, respectively. Apart from the small tensile stress at the connecting guide wall between the rigid cut-off wall and the plastic concrete cut-off wall, the cut-off wall is under pressure, especially the plastic cut-off wall. Combined with the analysis of the stress state of the wall, it can be determined that the anti-seepage wall (rigid cut-off wall and plastic concrete cut-off wall) is stable and safe during the construction period. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Hydraulic Engineering)
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