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Safety Assessment of Channel Seepage by Using Monitoring Data and Detection Information

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  • Mengdie Zhao

    (School of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
    Yellow River Engineering Consulting Co., Ltd., Zhengzhou 450003, China
    Henan Key Laboratory of Water Resources Conservation and Intensive Utilization in the Yellow River Basin, Zhengzhou 450008, China)

  • Chao Zhang

    (School of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou 450046, China)

  • Shoukai Chen

    (School of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
    Henan Key Laboratory of Water Resources Conservation and Intensive Utilization in the Yellow River Basin, Zhengzhou 450008, China)

  • Haifeng Jiang

    (College of Water Conservancy and Electric Power, Hohai University, Nanjing 210024, China)

Abstract

Seepage analysis has always been the focus of channel safety and stability research. Establishing a diagnosis method based on osmotic pressure monitoring data and combining the detection information to achieve osmotic safety is also an effective way to ensure the safety and stability of osmotic engineering. In this paper, a high-fill channel section of a water diversion project is taken as an example, and the study of osmotic safety is carried out by analyzing the engineering characteristics of linear engineering. High-fill channel sections were selected to study the temporal and spatial characteristics of various monitoring data reflecting the osmotic behavior of linear engineering; that is, these data reflect the time-varying regularity characteristics of the osmotic pressure value and the changing regularity of environmental variables. A single-point multifactor model of the monitoring data was established by establishing an evaluation index system, combining the monitoring index value method and the cloud model theory method according to the distribution law of the measured data and considering the uncertainty of the osmotic pressure data. Additionally, this model was integrated with the set pair analysis method to determine the monitoring data evaluation level; channel detection data information was collected, the abnormal detection of detection information was realized, and the monitoring data results were used to verify the detection results. In this way, an adaptive evaluation method reflecting the working behavior of high-filled channel sections is established, and a diagnostic technology for the safe operation of high-filled channel sections of linear engineering is proposed. The application results show that this method is suitable for engineering an osmotic safety assessment.

Suggested Citation

  • Mengdie Zhao & Chao Zhang & Shoukai Chen & Haifeng Jiang, 2022. "Safety Assessment of Channel Seepage by Using Monitoring Data and Detection Information," Sustainability, MDPI, vol. 14(14), pages 1-21, July.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:14:p:8378-:d:858543
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    References listed on IDEAS

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    1. Wei Ge & Zongkun Li & Robert Y. Liang & Wei Li & Yingchun Cai, 2017. "Methodology for Establishing Risk Criteria for Dams in Developing Countries, Case Study of China," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(13), pages 4063-4074, October.
    2. Xiaoling Wang & Hongling Yu & Peng Lv & Cheng Wang & Jun Zhang & Jia Yu, 2019. "Seepage Safety Assessment of Concrete Gravity Dam Based on Matter-Element Extension Model and FDA," Energies, MDPI, vol. 12(3), pages 1-21, February.
    3. Zhuguang Lan & Ming Huang, 2018. "Safety assessment for seawall based on constrained maximum entropy projection pursuit model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 91(3), pages 1165-1178, April.
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    1. Kun Xu & Shuang Li & Jiao Liu & Cheng Lu & Guangzhe Xue & Zhengquan Xu & Chao He, 2022. "Evaluation Cloud Model of Spontaneous Combustion Fire Risk in Coal Mines by Fusing Interval Gray Number and DEMATEL," Sustainability, MDPI, vol. 14(23), pages 1-13, November.

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