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Prediction of Permeability Coefficient k in Sandy Soils Using ANN

Author

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  • Grzegorz Wrzesiński

    (Institute of Civil Engineering, Warsaw University of Life Sciences, Nowoursynowska 159 St., 02-776 Warsaw, Poland)

  • Anna Markiewicz

    (Institute of Civil Engineering, Warsaw University of Life Sciences, Nowoursynowska 159 St., 02-776 Warsaw, Poland)

Abstract

The paper presents a method of application of an ANN (Artificial Neural Network) to predict the permeability coefficient k in sandy soils: FSa, MSa, CSa. To develop an ANN the results of permeability coefficients from pumping and consolidation tests were applied. The proposed ANN with an architecture 6-8-1 predicts the value of permeability coefficient k based on the following parameters: soil type, relative density I D , void ratio e and effective soil diameter d 10 . The mean relative error and single maximum value of the relative error for the proposed ANN are following: Mean RE = ±4%, Max RE = 7.59%. The use of the ANN to predict the soil permeability coefficient allows the reduction of the costs and time needed to conduct laboratory or field tests to determine this parameter.

Suggested Citation

  • Grzegorz Wrzesiński & Anna Markiewicz, 2022. "Prediction of Permeability Coefficient k in Sandy Soils Using ANN," Sustainability, MDPI, vol. 14(11), pages 1-13, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:11:p:6736-:d:828830
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    References listed on IDEAS

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    1. Dongwoo Jang & Hyoseon Park & Gyewoon Choi, 2018. "Estimation of Leakage Ratio Using Principal Component Analysis and Artificial Neural Network in Water Distribution Systems," Sustainability, MDPI, vol. 10(3), pages 1-13, March.
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    Cited by:

    1. Roman Trach & Victor Moshynskyi & Denys Chernyshev & Oleksandr Borysyuk & Yuliia Trach & Pavlo Striletskyi & Volodymyr Tyvoniuk, 2022. "Modeling the Quantitative Assessment of the Condition of Bridge Components Made of Reinforced Concrete Using ANN," Sustainability, MDPI, vol. 14(23), pages 1-19, November.

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