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

Deadline for manuscript submissions: 25 February 2025 | Viewed by 10180

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Special Issue Editors

Dr. Qiubing Ren

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Guest Editor

State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300350, China
Interests: water engineering safety management; structural health monitoring; structural condition assessment; engineering applications of artificial intelligence

Dr. Shaowei Wang

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Guest Editor

School of Urban Construction, Changzhou University, Changzhou 213164, China
Interests: dam safety management; structural health monitoring; aging diseases of concrete structures; concrete durability

Dr. Xudong Chen

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Guest Editor

School of Water Conservancy Engineering, Zhengzhou University, Zhengzhou 450001, China
Interests: structural health monitoring; water engineering management; big data processing; machine learning

Dr. Siyu Chen

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Guest Editor Assistant

Dam Safety Management Department, Nanjing Hydraulic Research Institute, Nanjing 210029, China
Interests: dam safety management; structural health monitoring; concrete dam; scientific machine learning; uncertainty quantification

Special Issue Information

Dear Colleagues,

A large amount of water engineering infrastructure, such as dams, embankments, and channels, was built to provide important societal and economic benefits related to ﬂood control, power generation, water supply, etc. It is particularly important to ensure safety in water engineering throughout the life cycle of design, construction, and operation. The present Special Issue focuses on water engineering safety and management in the hydraulics and hydrodynamics fields. We are pleased to invite you to submit your original research articles and reviews to this Special Issue.

Topics of interest include, but are not limited to, the following:

Water engineering simulation analysis theory;
Optimization design methods for water engineering;
Intelligent construction technology for water engineering;
Water engineering construction and safety management;
Water engineering safety monitoring and health diagnosis;
Water engineering reliability analysis and risk assessment;
Innovative simulation/computing tools for water engineering;
Novel operation and maintenance measures for water engineering.

Other studies related to water engineering safety and management in the hydraulics and hydrodynamics fields are also welcome.

We look forward to receiving your contributions.

Dr. Qiubing Ren
Dr. Shaowei Wang
Dr. Xudong Chen
Guest Editors

Dr. Siyu Chen
Guest Editor Assistant

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

water engineering
safety management
optimization design
intelligent construction
operation and maintenance
health diagnosis
risk assessment
numerical simulation
soft computing

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Published Papers (9 papers)

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Research

24 pages, 9147 KiB

Open AccessArticle

Efficient Productivity-Aware Control Parameter Optimization in Cutter Suction Dredger Construction Using Machine Learning with Parallel Global Search

by Hao Liu, Ruizhe Liu, Shuo Bai, Yong Chen and Leping Liu

Water 2024, 16(21), 3067; https://doi.org/10.3390/w16213067 - 25 Oct 2024

Viewed by 535

Abstract

This paper proposes an efficient productivity-aware optimization framework that utilizes hybrid machine learning with parallel global search to timely and appropriately adjust the critical control parameters (CCPs) of a cutter suction dredger (CSD) during construction. This optimization framework consists of three main parts. First, a hybrid Jaya–multilayer perceptron (MLP) algorithm was developed to rapidly construct a model that captures the interaction between construction parameters and slurry concentration. Next, the preliminary coarse results for the CCPs are determined through multi-parameter sensitivity analysis. Finally, the proposed resilient-zone parallel global search algorithm was employed to further optimize the CCPs, yielding more precise optimization results. To validate the proposed optimization framework and implement the in-situ service, it is applied to a real-world case study involving “Tianda” CSD construction. The results demonstrated that the average optimization duration is 6.7 s, which is shorter than the data acquisition interval of 8 s. Our approach improves the computational efficiency by 9.4 times compared with traditional optimization control methods. Additionally, there is a significant increase in the slurry concentration, with the maximum growth rate reaching 81.64%. Full article

(This article belongs to the Special Issue Water Engineering Safety and Management)

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15 pages, 6487 KiB

Open AccessArticle

Seismic Response Analysis of Hydraulic Tunnels Under the Combined Effects of Fault Dislocation and Non-Uniform Seismic Excitation

by Hao Liu, Wenyu Yan, Yingbo Chen, Jingyi Feng and Dexin Li

Water 2024, 16(21), 3060; https://doi.org/10.3390/w16213060 - 25 Oct 2024

Viewed by 382

Abstract

Hydraulic tunnels are prone to pass through faults and high-intensity earthquake areas, which will cause serious damage under fault dislocation and earthquake action. Fault dislocation and seismic excitation are often considered separately in previous studies. For tectonic earthquakes with higher frequency in seismic phenomena, fault dislocation and ground motion are often associated, and fault dislocation is usually the cause of earthquake occurrence, so it is limiting to consider the two separately. Moreover, strong earthquake records show that there will be significant differences in the mainland vibration within 50 m. The uniform ground motion inputs in previous studies are not suitable for long hydraulic tunnels. This paper begins with the simulation of non-uniform stochastic seismic excitations that consider spatial correlation. Based on stochastic vibration theory, multiple multi-point acceleration time-history curves that can reflect traveling wave effects, coherence effects, attenuation effects, and non-stationary characteristics are synthesized. Furthermore, a fault velocity function is introduced to account for the velocity effect of fault dislocation. Finally, numerical analyses of the response patterns of the tunnel lining under four different conditions are conducted based on an actual engineering project. The results indicate the following: (a) the maximum lining response values occur under the combined effects of fault dislocation and non-uniform seismic excitation, indicating its importance in the seismic resistance of the tunnel. (b) Compared to uniform seismic excitation, the peak displacement of the tunnel under non-uniform seismic excitation increases by up to 6.42%, and the peak maximum principal stress increases by up to 28%. Additionally, longer tunnels exhibit a noticeable delay effect in axial deformation during an earthquake. (c) Under non-uniform seismic excitation, the larger the fault dislocation magnitude, the greater the peak displacement and peak maximum principal stress at the monitoring points of the lining. The simulation results show that the extreme response values primarily occur at the crown and haunches of the tunnel, which require special attention. The research can provide valuable references for the seismic design of cross-fault tunnels. Full article

(This article belongs to the Special Issue Water Engineering Safety and Management)

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19 pages, 6516 KiB

Open AccessArticle

Predictions of Peak Discharge of Dam Failures Based on the Combined GA and BP Neural Networks

by Lv Ren, Yuan Tao, Jie Liu, Xin Jin, Changyuan Fan, Xiaohua Dong and Haiyan Wu

Water 2024, 16(20), 2946; https://doi.org/10.3390/w16202946 - 16 Oct 2024

Viewed by 491

Abstract

In this paper, the Artificial Neural Network (ANN) was utilized to predict the peak discharge of dam failures, which was based on the combined Genetic Algorithm (GA) and Back Propagation (BP) neural network. The dataset comprises 40 samples from self-conducted experiments and available literature. To compare the efficiency of the suggested approach, three evaluation metrics, including the coefficient of determination (R²), the root mean square error (RMSE) and the mean absolute error (MAE), were analyzed for both the BP neural network and the GA-BP neural network. The findings suggest that (1) The prediction accuracy of the GA-BP was better than that of the BP; and (2) Compared to BP, GA-BP demonstrated a 9.07% average improvement in R², a 57.36% average reduction in MAE, and a 57.53% average reduction in RMSE. In addition, the results of GA-BP and semi-empirical formulas were compared and the effect of three parameters on the peak discharge was analyzed. The results showed that the GA-BP model could effectively predict the peak discharge of dam failures. Full article

(This article belongs to the Special Issue Water Engineering Safety and Management)

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23 pages, 8605 KiB

Open AccessArticle

Displacement Interval Prediction Method for Arch Dam with Cracks: Integrated STL, MF-DFA and Bootstrap

by Zeyuan Chen, Bo Xu, Linsong Sun, Xuan Wang, Dalai Song, Weigang Lu and Yangtao Li

Water 2024, 16(19), 2755; https://doi.org/10.3390/w16192755 - 27 Sep 2024

Viewed by 393

Abstract

Displacement prediction models based on measured data have been widely applied in structural health monitoring. However, most models neglect the particularity of displacement monitoring for arch dams with cracks, nor do they thoroughly analyze the non-stationarity and uncertainty of displacement. To address this issue, the influencing factors of displacement were first considered, with crack opening displacement being incorporated into them, leading to the construction of the HSCT model that accounts for the effects of cracks. Feature selection was performed on the factors of the HSCT model utilizing the max-relevance and min-redundancy (mRMR) algorithm, resulting in the screened subset of displacement influence factors. Next, displacement was decomposed into trend, seasonal, and remainder components applying the seasonal-trend decomposition using loess (STL) algorithm. The multifractal characteristics of these displacement components were then analyzed by multifractal detrended fluctuation analysis (MF-DFA). Subsequently, displacement components were predicted employing the convolutional neural network-long short-term memory (CNN-LSTM) model. Finally, the impact of uncertainty factors was quantified using prediction intervals based on the bootstrap method. The results indicate that the proposed methods and models are effective, yielding satisfactory prediction accuracy and providing scientific basis and technical support for the health diagnosis of hydraulic structures. Full article

(This article belongs to the Special Issue Water Engineering Safety and Management)

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23 pages, 11212 KiB

Open AccessArticle

Field Investigations and Service Life Assessment of Geosynthetic Filters in Tidally Influenced Revetments in Cases of Ochre Clogging

by Lukas Tophoff, Holger Schüttrumpf, Frank Heimbecher, Christian Vollmer, Bodo Philipp and Yulduzkhon Abdullaeva

Water 2024, 16(13), 1840; https://doi.org/10.3390/w16131840 - 27 Jun 2024

Viewed by 795

Abstract

In recent decades, there have been individual cases of damage to the revetments of the German North Sea estuaries due to clogging by precipitated ochre products. This process is defined as ochre clogging and has been extensively explained in the literature. The primary aim of the investigations was to better understand the clogging process under in situ conditions and the causative environmental conditions surrounding the filters. Extensive in situ investigations were therefore carried out. It was found that a permeability reduction in geotextile filters can be strongly accelerated by ochre clogging, which appears to be a biogeochemical process. This describes a combined action of the chemical precipitation of iron and manganese, precipitation by microorganisms, and physical clogging. A further aim of this study was to establish limit values for the decisive ochre clogging parameters, which could be used to quantify the susceptibility to ochre clogging. It was shown that the determination of the iron and manganese content of the groundwater, as well as the redox capacity of the groundwater, is sufficient to assess the tendency for ochre clogging. To minimise the negative impact on filter performance, recommendations for an adapted filter design have been developed as a guide for planners. Full article

(This article belongs to the Special Issue Water Engineering Safety and Management)

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17 pages, 16229 KiB

Open AccessArticle

Deformation Monitoring and Potential Risk Detection of In-Construction Dams Utilizing SBAS-InSAR Technology—A Case Study on the Datengxia Water Conservancy Hub

by Yi Ouyang, Tao Feng, Han Feng, Xinghan Wang, Huayu Zhang and Xiaoxue Zhou

Water 2024, 16(7), 1025; https://doi.org/10.3390/w16071025 - 2 Apr 2024

Viewed by 1356

Abstract

Deformation monitoring plays a pivotal role in assessing dam safety. Interferometric Synthetic Aperture Radar (InSAR) has the advantage of obtaining an extensive range of deformation, regardless of weather conditions. The Datengxia Water Conservancy Hub is the largest in-construction dam in China. To effectively assess the in-construction dam safety, the SBAS-InSAR (Small Baseline Subset-InSAR) technique and 86 Sentinel-1 images (from 11 February 2020, to 16 January 2023) have been employed in this study to monitor the deformation over the reservoir and its surrounding areas. The reliability of the SBAS-InSAR monitoring results over the study area was demonstrated by the in situ monitoring results. And the InSAR results show that the central section of the left dam exhibits the most substantial cumulative deformation, attributed to the maximal water pressure. This is closely followed by the left end of the dam, which reflects a similar but smaller deformation. However, the in-construction cofferdam facilities make the right-end section of the left dam more robust, and the deformation is the most stable. Additionally, significant deformation of the auxiliary dam slope has been identified. Moreover, the analysis indicated that the deformation of the four upstream slopes is closely related to the precipitation, which potentially poses a threat to the safety of the Datengxia Dam. Full article

(This article belongs to the Special Issue Water Engineering Safety and Management)

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15 pages, 1682 KiB

Open AccessArticle

Study on the Calculation Method of Carbon Emissions in the Construction Industry: Targeting Small River Maintenance Projects in Korea

by Youngseok Song, Moojong Park and Jingul Joo

Water 2023, 15(20), 3608; https://doi.org/10.3390/w15203608 - 16 Oct 2023

Viewed by 2034

Abstract

The construction industry, responsible for approximately 30% of global carbon emissions, is closely linked to national development, making carbon reduction challenging. While national development is of paramount importance, it is essential to prioritize individual projects and establish a direction for reducing carbon emissions. The starting point should involve calculating the carbon emissions for each project and comprehending their quantitative impact. In this study, we calculated the carbon emissions for a small river maintenance project aimed at disaster prevention in the construction industry in Yongin-si, Gyeonggi-do, Korea. The total carbon emissions generated by the small river maintenance project in the target area amounted to 2016.6 tonCO₂. By process, the embankment construction was responsible for 789.7 tonCO₂, while the revetment construction contributed 1226.9 tonCO₂. The analysis revealed that the carbon emissions generated by the small river maintenance project equated to 10.2 tonCO₂/km of river length. Additionally, we developed an equation by applying the double-log function model (log–log) to small river length and carbon emissions. The coefficient of determination for the calculation equation is 0.42, which may not yield highly precise results. However, it is believed that this equation will provide a rough estimate of the carbon emissions associated with the small river maintenance project. Full article

(This article belongs to the Special Issue Water Engineering Safety and Management)

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17 pages, 1363 KiB

Open AccessArticle

Strong and Weak Supervision Combined with CLIP for Water Surface Garbage Detection

by Yunlin Ma, Zhenxiong Chu, Hao Liu, Ye Zhang, Chengzhao Liu, Dexin Li and Wei He

Water 2023, 15(17), 3156; https://doi.org/10.3390/w15173156 - 4 Sep 2023

Cited by 1 | Viewed by 1583

Abstract

Water surface garbage has a significant impact on the protection of water environments and ecological balance, making water surface garbage object detection a critical task. Traditional supervised object detection methods require a large amount of annotated data. To address this issue, we propose a method that combines strong and weak supervision with CLIP (Contrastive Language–Image Pretraining) for water surface garbage object detection. First, we train on a dataset annotated with strong supervision, using traditional object detection algorithms to learn the location information of water surface garbage. Then, we input the water surface garbage images into CLIP’s visual encoder to obtain visual feature representations. Simultaneously, we train CLIP’s text encoder using textual description annotations to obtain textual feature representations of the images. By fusing the visual and textual features, we obtain comprehensive feature representations. In the weak supervision training phase, we input the comprehensive feature representations into the object detection model and employ a training strategy that combines strong and weak supervision to detect and localize water surface garbage. To further improve the model’s performance, we introduce attention mechanisms and data augmentation techniques to enhance the model’s focus and robustness towards water surface garbage. By conducting experiments on two water surface garbage datasets, we validate the effectiveness of the proposed method based on the combination of strong and weak supervision with CLIP for water surface garbage object detection tasks. Full article

(This article belongs to the Special Issue Water Engineering Safety and Management)

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20 pages, 2349 KiB

Open AccessArticle

Joint Optimal Dispatch of Complex Urban Raw Water Supply: A Case Study of Lanxi City, Zhejiang Province, China

by Lingjie Li, Leizhi Wang, Xuan Gao, Xin Su, Yintang Wang and Rui Gao

Water 2023, 15(17), 3136; https://doi.org/10.3390/w15173136 - 31 Aug 2023

Viewed by 1161

Abstract

Water resources play a vital role in supporting urban economic and social development and ecological and environmental protection. Water shortage is a key factor that restricts the high-quality development of cities, while the coordinated and optimized allocation of urban raw water is an important measure to alleviate the water supply–demand imbalance. The current research on urban water supply issues and their underlying causes still needs to be further strengthened. Similarly, the formulation of rules for multi-source and cross-district water supply should pay more attention. This paper proposes a general analytical process consisting of four main stages: problem identification, system generalization, rule formulation, and model construction and solution for the joint optimal scheduling of raw water in a complex urban water supply system. This study investigates the significant water resource wastage and structural water shortage in the reservoirs of Lanxi City. The optimal scheduling plan is proposed by formulating rational rules for inter-district water supply and establishing a multi-source and multi-objective joint optimization scheduling model. Compared to the current independent scheduling scheme and multi-water source joint dispatching scheme based on the current dispatch diagram, the optimal scheduling plan effectively reduced the cumulative water shortage by 68.04 million m³ and 29.72 million m³, respectively, and increased the urban water supply guarantee rate in all districts of the city to over 90%. This study offers valuable insights to urban water resource managers, empowering them to develop optimal multiple water source supply rules that align to the specific characteristics of other case studies. Full article

(This article belongs to the Special Issue Water Engineering Safety and Management)

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