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Hydraulic Engineering Applications of Artificial Intelligence, Deep Learning, and Digital...
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Hydraulic Engineering Applications of Artificial Intelligence, Deep Learning, and Digital Twin Technology

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

Deadline for manuscript submissions: 10 February 2025 | Viewed by 3233

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. 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. Yating Hu

E-Mail Website
Guest Editor
School of Civil Engineering and Architecture, Nanchang University, Nanchang 330031, China
Interests: structural engineering; safety engineering; civil engineering; artificial neural network; artificial intelligence; data mining
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

Special Issue Information

Dear Colleagues,

With the continuous developments in intelligent hydraulic engineering, artificial intelligence and deep learning methods have been widely used in this field for automatic data perception, intelligent processing, storage, and analysis. The emergence of digital twin technology provides technical support for intelligent water conservancy project construction, covering functions ranging from forecasting to early warning, previewing, and preplanning and providing forward-looking, scientific, precise, and safe support for decision-making and management. Combined with traditional hydraulic engineering safety monitoring methods, such as geotechnical tests and numerical simulation, artificial intelligence algorithms, deep learning methods, and digital twin technology can help solve more complex problems, providing more accurate and professional intelligent analyses and ubiquitous services, which is of great theoretical significance and application value, ensuring project safety. Therefore, this Special Issue will focus on artificial intelligence, deep learning methods, and digital twin technology in hydraulic engineering construction. We would like to invite you to submit your research papers on suitable topics including but not limited to the following: hydraulic engineering information perception, intelligent processing methods of safety monitoring data, application of digital twin technology in hydraulic engineering, intelligent safety monitoring models, and systems of hydraulic engineering.

Dr. Chenfei Shao
Dr. Hao Gu
Dr. Yanxin Xu
Dr. Xiangnan Qin
Dr. Yating Hu
Dr. Huixiang Chen
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
  • deep learning
  • digital twin technology
  • safety monitoring
  • hydraulic engineering
  • safety monitoring data preprocessing

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

16 pages, 3809 KiB  
Article
A Dam Displacement Prediction Method Based on a Model Combining Random Forest, a Convolutional Neural Network, and a Residual Attention Informer
by Chunhui Fang, Ying Jiao, Xue Wang, Taiqi Lu and Hao Gu
Water 2024, 16(24), 3687; https://doi.org/10.3390/w16243687 - 20 Dec 2024
Viewed by 634
Abstract
To enhance the accuracy of dam displacement prediction, this paper proposes a hybrid model combining Random Forest (RF), a Convolutional Neural Network (CNN), and a Residual Attention Informer (RA-Informer). Firstly, RF is utilized to assess the importance of input features, selecting key factors [...] Read more.
To enhance the accuracy of dam displacement prediction, this paper proposes a hybrid model combining Random Forest (RF), a Convolutional Neural Network (CNN), and a Residual Attention Informer (RA-Informer). Firstly, RF is utilized to assess the importance of input features, selecting key factors that significantly influence dam displacement. Then, CNN is employed to perform deep feature extraction on the input data, mining effective information. Subsequently, the Informer model integrated with a residual attention mechanism establishes the mapping relationship between the extracted features and dam displacement, enhancing the focus on critical features. Finally, the Northern Goshawk Optimization (NGO) algorithm is adopted to optimize the model’s hyperparameters. Experimental results on actual engineering data demonstrate that the proposed model exhibits superior prediction accuracy and stability compared to other typical models, offering higher precision and reliability. Full article
(This article belongs to the Special Issue Hydraulic Engineering Applications of Artificial Intelligence, Deep Learning, and Digital Twin Technology)
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20 pages, 8443 KiB  
Article
Inversion Model for Permeability Coefficient Based on Random Forest–Secretary Bird Optimization Algorithm: Case Study of Lower Reservoir of C-Pumped Storage Power Station
by Zekai Ma, Zhenzhong Shen and Jiangyin Yang
Water 2024, 16(21), 3096; https://doi.org/10.3390/w16213096 - 29 Oct 2024
Viewed by 891
Abstract
The geological complexity of the karst regions presents significant challenges, with the permeability coefficient being a critical parameter for accurately analyzing seepage behavior in hydraulic engineering projects. To overcome the limitations of traditional inversion methods, which often exhibit low computational efficiency, poor accuracy, [...] Read more.
The geological complexity of the karst regions presents significant challenges, with the permeability coefficient being a critical parameter for accurately analyzing seepage behavior in hydraulic engineering projects. To overcome the limitations of traditional inversion methods, which often exhibit low computational efficiency, poor accuracy, and instability, this study utilizes a finite-element forward model and orthogonal experimental design to establish a sample set for permeability-coefficient inversion. A surrogate model for seepage calculation based on the Random Forest (RF) algorithm is subsequently developed. Furthermore, the Secretary Bird Optimization Algorithm (SBOA) is incorporated to propose an intelligent RF–SBOA inversion method for permeability-coefficient estimation, which is validated through a case study of the C-pumped storage power station. The results demonstrate that the RF model’s predictions for water levels at four boreholes closely align with the measured data, outperforming models such as CART, BP, and SVR. The SBOA effectively identifies the optimal geological permeability coefficient, with the borehole water-level inversion achieving a maximum relative error of only 0.128%, which meets the accuracy requirements for engineering applications. Additionally, the computed distribution of the natural seepage field is consistent with the typical distribution patterns observed in mountain seepage systems. During the normal water-storage phase, both the calculated seepage flow and gradient comply with engineering standards, while the seepage-field distribution aligns with empirical observations. This inversion model provides a rapid and accurate method for estimating the permeability coefficient of strata in the project area, with potential applicability to permeability inversion in other engineering geology contexts, thus demonstrating considerable practical value for engineering applications. Full article
(This article belongs to the Special Issue Hydraulic Engineering Applications of Artificial Intelligence, Deep Learning, and Digital Twin Technology)
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21 pages, 6957 KiB  
Article
Inversion Method for Material Parameters of Concrete Dams Using Intelligent Algorithm-Based Displacement Separation
by Jianrong Xu, Lingang Gao, Tongchun Li, Jinhua Guo, Huijun Qi, Yu Peng and Jianxin Wang
Water 2024, 16(20), 2979; https://doi.org/10.3390/w16202979 - 18 Oct 2024
Viewed by 1153
Abstract
Integrating long-term observational data analysis with numerical simulations of dam operations provides an effective approach to dam safety evaluation. However, analytical results are often subject to errors due to challenges in accurately surveying and modeling the foundation, as well as temporal changes in [...] Read more.
Integrating long-term observational data analysis with numerical simulations of dam operations provides an effective approach to dam safety evaluation. However, analytical results are often subject to errors due to challenges in accurately surveying and modeling the foundation, as well as temporal changes in foundation properties. This paper proposes a concrete dam displacement separation model that distinguishes between deformation caused by foundation restraint and that induced by external loads. By combining this model with intelligent optimization techniques and long-term observational data, we can identify the actual mechanical parameters of the dam and conduct structural health assessments. The proposed model accommodates multiple degrees of freedom and is applicable to both two- and three-dimensional dam modeling. Consequently, it is well-suited for parameter identification and health diagnosis of concrete gravity and arch dams with extensive observational data. The efficacy of this diagnostic model has been validated through computational case studies and practical engineering applications. Full article
(This article belongs to the Special Issue Hydraulic Engineering Applications of Artificial Intelligence, Deep Learning, and Digital Twin Technology)
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