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Seepage Safety Assessment of Concrete Gravity Dam Based on Matter-Element Extension Model and FDA

Author

Listed:
  • Xiaoling Wang

    (State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300072, China)

  • Hongling Yu

    (State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300072, China)

  • Peng Lv

    (State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300072, China)

  • Cheng Wang

    (State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300072, China)

  • Jun Zhang

    (State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300072, China)

  • Jia Yu

    (State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300072, China)

Abstract

As an important infrastructure project, the concrete gravity dam plays an extremely important role in hydropower generation, irrigation, flood control, and other aspects. Seepage is an important factor affecting the stability of concrete gravity dams. Seepage safety assessment is of great significance to the safe operation of the dams. However, the existing seepage safety assessment models are not dynamic, and the correlation among indicators is often neglected and the overall seepage safety of the concrete gravity dams has not been considered. To solve these problems, this research proposes a dynamic matter-element extension (D-MEE) model. First, the D-MEE model is established through adroit integration of the matter-element extension (MEE) model and functional data analysis (FDA). Second, a dynamic criteria importance through the intercriteria correlation (D-CRITIC) method that can effectively consider the correlation among indicators is proposed to determine the weights. Third, the influence of different dam blocks on the overall seepage safety status is considered by constructing a spatial weight matrix. Finally, the proposed method is applied to the concrete gravity dam X in southwest China. The results show that the proposed method is effective and superior to the existing evaluation methods of seepage safety.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:3:p:502-:d:203602
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    References listed on IDEAS

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    Cited by:

    1. Jun Dong & Dongxue Wang & Dongran Liu & Palidan Ainiwaer & Linpeng Nie, 2019. "Operation Health Assessment of Power Market Based on Improved Matter-Element Extension Cloud Model," Sustainability, MDPI, vol. 11(19), pages 1-25, October.
    2. Muhammad Ishfaque & Qianwei Dai & Nuhman ul Haq & Khanzaib Jadoon & Syed Muzyan Shahzad & Hammad Tariq Janjuhah, 2022. "Use of Recurrent Neural Network with Long Short-Term Memory for Seepage Prediction at Tarbela Dam, KP, Pakistan," Energies, MDPI, vol. 15(9), pages 1-16, April.
    3. 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.

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