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Safety Monitoring of Hydraulic Structures
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Safety Monitoring of Hydraulic Structures

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

Deadline for manuscript submissions: closed (25 December 2024) | Viewed by 8617

Special Issue Editor

Prof. Dr. Zhao Erfeng

E-Mail Website
Guest Editor
State Key Laboratory of Hydrology Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China
Interests: hydraulic structure; monitoring; risk assessment; health diagnosis; numerical simulation; model test; data mining; modelling

Special Issue Information

Dear Colleagues,

The theme of the Special Issue is ‘Safety Monitoring of Hydraulic Structures’. It mainly focuses on the research fields of ‘Structural Health Monitoring Methods and Techniques’, ’Computation Theories and Experimental Techniques’, ’ Materials and Reinforcement’, and ’Intelligent Construction’. It is dedicated to providing experts, scholars, engineers, etc., from different colleges and universities, research institutes, enterprises and institutions from home and abroad with an academic platform to share academic research findings, explore cutting-edge engineering issues and discuss current opportunities and challenges in a concerted effort to promote international cooperation and communication and the industrialization of scientific research results.

Prof. Dr. Zhao Erfeng
Guest Editor

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Keywords

  • hydraulic structure
  • safety monitoring
  • sensing technology
  • data mining
  • modelling
  • reliability analysis
  • failure simulation
  • new materials for sensing
  • performance improvement
  • artificial intelligence

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Published Papers (5 papers)

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Research

12 pages, 4355 KiB  
Article
Effect of Seepage on Sand Levee Failure Due to Lateral Overtopping
by Woochul Kang, Seongyun Kim and Eunkyung Jang
Water 2024, 16(24), 3617; https://doi.org/10.3390/w16243617 - 16 Dec 2024
Viewed by 767
Abstract
Recent increases in rainfall duration and intensity due to climate change have heightened the importance of levee stability. However, previous studies on levee failure, primarily caused by seepage and overtopping, have mostly examined these causes independently owing to their distinct characteristics. In this [...] Read more.
Recent increases in rainfall duration and intensity due to climate change have heightened the importance of levee stability. However, previous studies on levee failure, primarily caused by seepage and overtopping, have mostly examined these causes independently owing to their distinct characteristics. In this study, we conducted lateral overtopping failure experiments under seepage conditions that closely resembled those in experiments conducted in previous studies. Seepage was monitored using water pressure sensors and a distributed optical fiber cable that provided continuous heat for temperature monitoring in the levee. Τhe analysis of levee failure due to lateral overtopping, in the presence of seepage, was conducted using image analysis with digitization techniques and machine learning-based color segmentation techniques on the protected lowland side of the levee, targeting the same area. The results revealed that levee failure occurred more than twice as fast in experiments where seepage conditions were considered compared to the experiments where they were not. Thus, levees weakened by seepage are more vulnerable to overtopping and breaching. Consequently, employing a comprehensive approach that integrates various monitoring and analysis methods for assessing levee stability is preferable to relying on a single method alone. Full article
(This article belongs to the Special Issue Safety Monitoring of Hydraulic Structures)
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39 pages, 18258 KiB  
Article
Structural Health Monitoring and Failure Analysis of Large-Scale Hydro-Steel Structures, Based on Multi-Sensor Information Fusion
by Helin Li, Huadong Zhao, Yonghao Shen, Shufeng Zheng and Rui Zhang
Water 2024, 16(22), 3167; https://doi.org/10.3390/w16223167 - 5 Nov 2024
Viewed by 1857
Abstract
Large-scale hydro-steel structures (LS-HSSs) are vital to hydraulic engineering, supporting critical functions such as water resource management, flood control, power generation, and navigation. However, due to prolonged exposure to severe environmental conditions and complex operational loads, these structures progressively degrade, posing increased risks [...] Read more.
Large-scale hydro-steel structures (LS-HSSs) are vital to hydraulic engineering, supporting critical functions such as water resource management, flood control, power generation, and navigation. However, due to prolonged exposure to severe environmental conditions and complex operational loads, these structures progressively degrade, posing increased risks over time. The absence of effective structural health monitoring (SHM) systems exacerbates these risks, as undetected damage and wear can compromise safety. This paper presents an advanced SHM framework designed to enhance the real-time monitoring and safety evaluation of LS-HSSs. The framework integrates the finite element method (FEM), multi-sensor data fusion, and Internet of Things (IoT) technologies into a closed-loop system for real-time perception, analysis, decision-making, and optimization. The system was deployed and validated at the Luhun Reservoir spillway, where it demonstrated stable and reliable performance for real-time anomaly detection and decision-making. Monitoring results over time were consistent, with stress values remaining below allowable thresholds and meeting safety standards. Specifically, stress monitoring during radial gate operations (with a current water level of 1.4 m) indicated that the dynamic stress values induced by flow vibrations at various points increased by approximately 2 MPa, with no significant impact loads. Moreover, the vibration amplitude during gate operation was below 0.03 mm, confirming the absence of critical structural damage and deformation. These results underscore the SHM system’s capacity to enhance operational safety and maintenance efficiency, highlighting its potential for broader application across water conservancy infrastructure. Full article
(This article belongs to the Special Issue Safety Monitoring of Hydraulic Structures)
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14 pages, 4437 KiB  
Article
A New Numerical Method to Evaluate the Stability of Dike Slope Considering the Influence of Backward Erosion Piping
by Zhen Ma, Xiaobing Wang, Ning Shang and Qing Zhang
Water 2024, 16(12), 1706; https://doi.org/10.3390/w16121706 - 15 Jun 2024
Cited by 1 | Viewed by 1162
Abstract
Backward erosion piping, a soil erosion phenomenon induced by seepage, compromises the stability of water-retaining structures such as dikes. During floods, the seepage in the dike body increases due to high water levels, which directly affects the progression of the piping channel. The [...] Read more.
Backward erosion piping, a soil erosion phenomenon induced by seepage, compromises the stability of water-retaining structures such as dikes. During floods, the seepage in the dike body increases due to high water levels, which directly affects the progression of the piping channel. The formation of the piping channel then impacts the stability of the dike. In this paper, an improved piping model that considers the impact of seepage in the dike body is proposed based on Wewer’s model. Specifically, we added a seepage field of the dike body to the original model to account for the impact of dike-body seepage on the evolution of piping. The seepage field of the dike body is solved using Darcy’s law and the continuity equation for unsaturated porous media. In addition, this approach also incorporates the coupling effect of seepage stress. The accuracy of the model was verified through comparing the calculated results with the IJkdijk experiment and Wewer’s results. The effects of BEP on dike stability were investigated using the proposed improved piping model. The two major conclusions of the study are that (1) the incorporation of unsaturated seepage enhanced the performance of the piping model, allowing it to more accurately simulate the development of pipe length and the changing of pore pressure; and (2) the formation of the pipe impacted dike stability, leading to a substantial reduction in the safety factor of the dike slope. Full article
(This article belongs to the Special Issue Safety Monitoring of Hydraulic Structures)
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22 pages, 16640 KiB  
Article
Deformation Field Analysis of Small-Scale Model Experiment on Overtopping Failure of Embankment Dams
by Qiang Lu, Yanchang Gu, Shijun Wang, Xiandong Liu and Hong Wang
Water 2023, 15(24), 4309; https://doi.org/10.3390/w15244309 - 18 Dec 2023
Cited by 3 | Viewed by 1723
Abstract
There are a large number of reservoir dams in China, of which embankment dams account for more than 90%, and public safety will be seriously endangered in case of dam failure. Overtopping is the leading cause of dam failure, and the existing research [...] Read more.
There are a large number of reservoir dams in China, of which embankment dams account for more than 90%, and public safety will be seriously endangered in case of dam failure. Overtopping is the leading cause of dam failure, and the existing research mainly focuses on the study of the failure process, with less research on the change in the deformation field during the failure process. In this study, the measured deformation field data of a modeled embankment dam during the whole process of impoundment, operation, and failure were obtained by carrying out indoor small-scale model experiments of overtopping failure, embedding inclinometers inside the dam body, and setting vertical displacement measurement markers on the surface. A refined analysis of the measured deformation data shows that the dam body displaces vertically downward during the impoundment stage and the vertical displacement at the dam crest has the largest amplitude; the internal horizontal displacement changes to the left bank and downstream side, and the amplitude of the internal horizontal displacement (upstream and downstream direction and dam axis direction) on the right dam sections is more significant than that in the middle of the dam; during the breaching stage, the time sequence of the sudden change in each internal horizontal displacement measuring point is from the downstream side to the upstream side and from the higher elevation to the lower elevation, which is basically consistent with the process of overtopping of embankment dams; and the overall sudden change in left and right bank horizontal displacements within the downstream side of the dam crest and the downstream side of the dam body gauges is significant, and the sudden change in upstream and downstream horizontal displacement (U&D HD) within the downstream side of the dam crest gauges is significant. The experimental analysis results can support the disaster mechanism of embankment dam failure and the theory of early warning of failure. Full article
(This article belongs to the Special Issue Safety Monitoring of Hydraulic Structures)
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23 pages, 7030 KiB  
Article
Stress Prediction Model of Super-High Arch Dams Based on EMD-PSO-GPR Model
by Chunyao Hou, Yilun Wei, Hongyi Zhang, Xuezhou Zhu, Dawen Tan, Yi Zhou and Yu Hu
Water 2023, 15(23), 4087; https://doi.org/10.3390/w15234087 - 24 Nov 2023
Cited by 3 | Viewed by 1608
Abstract
In response to the challenge of limited model availability for predicting the lifespan of super-high arch dams, a hybrid model named EMD-PSO-GPR (EPR) is proposed in this study. The EPR model leverages Empirical Mode Decomposition (EMD), Gaussian Process Regression (GPR), and Particle Swarm [...] Read more.
In response to the challenge of limited model availability for predicting the lifespan of super-high arch dams, a hybrid model named EMD-PSO-GPR (EPR) is proposed in this study. The EPR model leverages Empirical Mode Decomposition (EMD), Gaussian Process Regression (GPR), and Particle Swarm Optimization (PSO) to provide an effective solution for super-high arch dam stress prediction. This research focuses on three strategically selected measurement points within the dam, characterized by complex stress conditions. The predicted results from the EPR are compared with those from GPR, Long Short-Term Memory (LSTM), and Support Vector Regression (SVR), using actual stress data measured at research points within a super-high arch dam in Southwest China. The findings reveal that the proposed EPR model attains a maximum mean absolute error (MAE) of 0.02916 and a maximum root mean square error (RMSE) of 0.03055, surpassing the compared models. As a result, the EPR model introduces an innovative computational framework for stress prediction in super-high arch dams, excelling in handling stress data characterized by high vibration frequencies and providing more accurate predictions. Full article
(This article belongs to the Special Issue Safety Monitoring of Hydraulic Structures)
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