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Intelligent Safety Diagnosis and Reinforcement of Water-Related Buildings
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Intelligent Safety Diagnosis and Reinforcement of Water-Related Buildings

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

Deadline for manuscript submissions: 15 December 2025 | Viewed by 2937

Special Issue Editors

Dr. Yangtao Li

E-Mail Website
Guest Editor
1. College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China
2. The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing 210037, China
3. College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210037, China
Interests: structural health monitoring; computer vision; structural damage detection; non-contact inspection; artificial intelligence
Prof. Dr. Yang Wei

E-Mail Website
Guest Editor
School of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China
Interests: hydraulic concrete materials; underwater inspection and reinforcement of water-related structures; bridge detection and reinforcement technology; bamboo (wood) structure; new composite structure; concrete structural damage detection
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

Special Issue Information

Dear Colleagues,

Water-related buildings exhibit characteristics such as remote geographical locations, numerous installations, and dispersed distributions. The methods for information transmission vary significantly. Traditional small and medium-sized water-related buildings are primarily monitored through manual techniques, leading to infrequent monitoring and the involvement of inadequately trained personnel. This results in substantial information transmission delays, creating challenges for management units. The introduction and implementation of safety monitoring and detection systems for water-related buildings offer innovative approaches to safety management. The long-term operation of automated monitoring and detection systems generates vast amounts of data, providing a solid foundation for the exploration of novel methodologies utilizing artificial intelligence and deep learning. AI technology holds significant promise in the realm of damage intelligent diagnosis and damage reinforcement of water-related buildings, which constitutes the central focus of this special issue.

Dr. Yangtao Li
Prof. Dr. Yang Wei
Dr. Hao Gu
Guest Editors

Manuscript Submission Information

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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

  • dams, breakwaters and reservoirs
  • hydraulic structures
  • hydropower refurbishment
  • smart diagnosis
  • structural safety
  • hydraulic rehabilitation
  • dike & coastal defense safety
  • intelligent management & reinforcement
  • machine learning & deep learning

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Published Papers (4 papers)

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Research

17 pages, 4100 KiB  
Article
Outlier Identification of Concrete Dam Displacement Monitoring Data Based on WAVLET-DBSCAN-IFRL
by Chunhui Fang, Xue Wang, Weixing Hu, Xiaojun He, Zihui Huang and Hao Gu
Water 2025, 17(5), 716; https://doi.org/10.3390/w17050716 - 28 Feb 2025
Viewed by 535
Abstract
Monitoring data outliers comprises isolated mode outliers, cluster mode outliers, and normal points. To identify and distinguish the data hopping problems caused by outliers and environmental mutations in the displacement monitoring data of concrete AMS, this paper proposes a method based on wavelet [...] Read more.
Monitoring data outliers comprises isolated mode outliers, cluster mode outliers, and normal points. To identify and distinguish the data hopping problems caused by outliers and environmental mutations in the displacement monitoring data of concrete AMS, this paper proposes a method based on wavelet transform, DBSCAN clustering algorithm combined with isolated forest and reinforcement learning algorithm to identify outliers in concrete dam monitoring data. In this paper, the trend line of measuring point data are extracted by the wavelet transform algorithm, and the residual data are obtained by subtracting it from the original process line. Subsequently, the DBSCAN clustering algorithm divides the residual data according to density. Therewith, the outlier scores of different data clusters are calculated, the iterative Q values are updated, and the threshold values are set. The data exceeding the threshold are finally marked as outliers. Finally, the water level and displacement data were compared by drawing the trend to ensure that the water level change did not cause the final identified concrete dam displacement data outliers. The results of the example analysis show that compared with the other two outlier detection methods (“Wavelet transform combined with DBSCAN clustering” or “W-D method”, “Wavelet transform combined with isolated forest method” or “W-IF method”). The method has the lowest error rate and the highest precision rate, recall rate, and F1 score. The error rate, precision rate, recall rate, and F1 score were 0.0036, 0.870, 1.000, and 0.931, respectively. This method can effectively identify data jumps caused by an environmental mutation in deformation monitoring data, significantly improve the accuracy of outlier identification, reduce the misjudgement rate of outliers, and have the highest detection accuracy. Full article
(This article belongs to the Special Issue Intelligent Safety Diagnosis and Reinforcement of Water-Related Buildings)
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21 pages, 5736 KiB  
Article
Characteristics of Creep and Permeability Changes in Coal Samples from Underground Water Storage Structures Under High Stresses
by Zichang Liu, Yinghu Li, Kaifang Fan, Shijun Wang, Yanchang Gu, Ze Xia and Qiangling Yao
Water 2025, 17(4), 538; https://doi.org/10.3390/w17040538 - 13 Feb 2025
Viewed by 474
Abstract
Underground reservoirs are a key technology for storing mine-impacted water resources, and the long-term stability of their coal pillar dams in high-stress environments is critical. The long-term safety of coal pillar dams in such reservoirs is closely related to creep and water seepage [...] Read more.
Underground reservoirs are a key technology for storing mine-impacted water resources, and the long-term stability of their coal pillar dams in high-stress environments is critical. The long-term safety of coal pillar dams in such reservoirs is closely related to creep and water seepage phenomena. To better illustrate this phenomenon, internal expansion coefficients and porosity blocking coefficients are proposed in this study to characterize how water affects the evolution of permeability in water-bearing coal samples. A novel model is developed to capture the interaction between matrix and fractures and the influence of creep deformation on permeability in water-bearing coal samples. Triaxial creep–seepage experiments are conducted on raw coal samples with varying moisture content. The results show that volumetric strain values and strain rates increase with rising effective stress during creep and show a tendency to first increase and then decrease with the increase in moisture content. Additionally, permeability consistently decreases at each stage of creep. Model parameters are determined through the nonlinear least squares method, and the reliability of the permeability model is validated based on experimental data. Both theoretical modeling and experimental results indicate that water seepage–creep coupling significantly affects the long-term strength of coal samples in a high-stress environment, and corresponding prevention and control measures are suggested. This study can provide a scientific basis and guidance for the study of long-term operational destabilization damage of coal mine underground reservoirs to ensure the safety of the structure. Full article
(This article belongs to the Special Issue Intelligent Safety Diagnosis and Reinforcement of Water-Related Buildings)
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20 pages, 7058 KiB  
Article
A Novel Intelligent Learning Method for Identifying Gross Errors in Dam Deformation Monitoring Series
by Chunhui Fang, Xue Wang, Jianchao Li, Luobin Wu, Jiayi Wang and Hao Gu
Water 2025, 17(2), 148; https://doi.org/10.3390/w17020148 - 8 Jan 2025
Viewed by 614
Abstract
In view of the problem that traditional dam outlier identification methods mostly rely on single-monitoring-point models and do not fully consider the spatio-temporal correlation characteristics of deformation between monitoring points, which can easily lead to the misdiagnosis of outliers, this paper proposes a [...] Read more.
In view of the problem that traditional dam outlier identification methods mostly rely on single-monitoring-point models and do not fully consider the spatio-temporal correlation characteristics of deformation between monitoring points, which can easily lead to the misdiagnosis of outliers, this paper proposes a novel Ward-VMD-BiLSTM-Iforest method for identifying gross errors in dam deformation monitoring. By integrating spatio-temporal clustering, variational mode decomposition (VMD), and BiLSTM neural networks, the method effectively identifies outliers while avoiding the misclassification of data mutations caused by environmental changes. Compared to traditional models (GRU, LSTM, and BiLSTM), the HHO-BiLSTM model demonstrates superior performance, achieving an R2 of 0.97775 at TCN08, with a reduced MAE and better accuracy. In comparison with the Raida and Romanovsky criteria, the proposed method achieves 100% precision and 100% recall, significantly improving detection accuracy and reducing misjudgment. This method provides an effective and reliable solution for dam deformation outlier detection. Full article
(This article belongs to the Special Issue Intelligent Safety Diagnosis and Reinforcement of Water-Related Buildings)
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19 pages, 9295 KiB  
Article
Impact of Water-Induced Corrosion on the Structural Security of Transmission Line Steel Pile Poles
by Wansong Bai, Lang Li, Chun Yang, Yahui Zhang, Dan Song and Feng Lv
Water 2024, 16(24), 3581; https://doi.org/10.3390/w16243581 (registering DOI) - 12 Dec 2024
Viewed by 766
Abstract
In addressing the impact of corrosion on the structural integrity of steel transmission line poles, this study explores the variation in load-bearing capacity under water-related corrosion conditions using the finite element method. The analysis focuses on how corrosion at the base and cross-arm [...] Read more.
In addressing the impact of corrosion on the structural integrity of steel transmission line poles, this study explores the variation in load-bearing capacity under water-related corrosion conditions using the finite element method. The analysis focuses on how corrosion at the base and cross-arm components of steel poles affects their mechanical performance and modal response. The investigation extends to evaluating the structural safety of steel poles under varying levels of water-induced corrosion, specifically considering combined wind load and broken-line load impacts through static equivalent analysis. The corrosion extent is quantified by the material mass loss rate, with material property degradation applied to simulate corrosion effects. Findings reveal that increased corrosion depth and length result in the concentration of stress and strain at affected areas, alongside decreased vibration frequencies, heightening resonance risk under wind loads. Furthermore, as the mass loss rate increases, maximum equivalent stress and elastic strain values rise significantly. This research provides a scientific basis for understanding water-related corrosion effects on steel transmission line poles, offering essential theoretical insights to enhance structural safety. Full article
(This article belongs to the Special Issue Intelligent Safety Diagnosis and Reinforcement of Water-Related Buildings)
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