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A New Method for Pore Pressure Prediction on Malfunctioning Cells Using Artificial Neural Networks

Author

Listed:
  • Milica Markovic

    (University of Nis)

  • Jelena Markovic Brankovic

    (University of Nis)

  • Miona Andrejevic Stosovic

    (University of Nis)

  • Srdjan Zivkovic

    (University of Nis)

  • Bojan Brankovic

    (Adesso Austria GmbH)

Abstract

Embankment rockfill dams are the most common dam construction types used in the world today. One third of all embankment dam failures are caused by dam slope instability. The dam is stable when the slopes are stable. Slope safety of the dam is assessed through pore and total pressure data analysis registered on pressure measurement cells installed in the dam. During the service life of a dam, one or more cells may malfunction after years of operation. Cell replacement implies economically unjustified high costs and is usually technically impossible and high risk. In this paper, the problem of a malfunctioning cell with a small available dataset is analysed. A new method for pore pressure prediction on malfunctioning cells has been developed using several successive artificial neural networks (ANNs) to obtain high accuracy of the predicted values. The results show that these predicted values are more precise than values we could have obtained using only one artificial neural network for prediction.

Suggested Citation

  • Milica Markovic & Jelena Markovic Brankovic & Miona Andrejevic Stosovic & Srdjan Zivkovic & Bojan Brankovic, 2021. "A New Method for Pore Pressure Prediction on Malfunctioning Cells Using Artificial Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(3), pages 979-992, February.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:3:d:10.1007_s11269-021-02763-0
    DOI: 10.1007/s11269-021-02763-0
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    References listed on IDEAS

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    1. Parisa Noorbeh & Abbas Roozbahani & Hamid Kardan Moghaddam, 2020. "Annual and Monthly Dam Inflow Prediction Using Bayesian Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(9), pages 2933-2951, July.
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    4. Peiman Parisouj & Hamid Mohebzadeh & Taesam Lee, 2020. "Employing Machine Learning Algorithms for Streamflow Prediction: A Case Study of Four River Basins with Different Climatic Zones in the United States," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(13), pages 4113-4131, October.
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