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The Use of Artificial Neural Networks for Determining Values of Selected Strength Parameters of Miscanthus × Giganteus

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  • Sławomir Francik

    (Department of Mechanical Engineering and Agrophysics, Faculty of Production Engineering and Energetics, University of Agriculture in Krakow, Balicka 120, 30-149 Krakow, Poland)

  • Bogusława Łapczyńska-Kordon

    (Department of Mechanical Engineering and Agrophysics, Faculty of Production Engineering and Energetics, University of Agriculture in Krakow, Balicka 120, 30-149 Krakow, Poland)

  • Norbert Pedryc

    (Department of Mechanical Engineering and Agrophysics, Faculty of Production Engineering and Energetics, University of Agriculture in Krakow, Balicka 120, 30-149 Krakow, Poland)

  • Wojciech Szewczyk

    (Department of Agroecology and Plant Production, University of Agriculture in Krakow, Al. Mickiewicza 21, 31-120 Krakow, Poland)

  • Renata Francik

    (Department of Bioorganic Chemistry, Chair of Organic Chemistry, Jagiellonian University Medical College, 30-688 Krakow, Poland
    Institute of Health, State Higher Vocational School, Staszica 1, 33-300 Nowy Sącz, Poland)

  • Zbigniew Ślipek

    (Department of Mechanical Engineering and Agrophysics, Faculty of Production Engineering and Energetics, University of Agriculture in Krakow, Balicka 120, 30-149 Krakow, Poland
    Technical Institute, State Higher Vocational School, Staszica 1, 33-300 Nowy Sącz, Poland)

Abstract

The aim of this paper is to develop neural models enabling the determination of biomechanical parameters for giant miscanthus stems. The static three-point bending test is used to determine the bending strength parameters of the miscanthus stem. In this study, we assume the modulus of elasticity bending and maximum stress in bending as the dependent variables. As independent variables (inputs of the neural network) we assume water content, internode number, maximum bending force value and dimensions characterizing the cross-section of miscanthus stem: maximum and minimum stem diameter and stem wall thickness. The four developed neural models, enabling the determination of the value of the modulus of elasticity in bending and the maximum stress in bending, demonstrate sufficient and even very high accuracy. The neural networks have an average relative error of 2.18%, 2.21%, 3.24% and 0.18% for all data subsets, respectively. The results of the sensitivity analysis confirmed that all input variables are important for the accuracy of the developed neural models—correct semantic models.

Suggested Citation

  • Sławomir Francik & Bogusława Łapczyńska-Kordon & Norbert Pedryc & Wojciech Szewczyk & Renata Francik & Zbigniew Ślipek, 2022. "The Use of Artificial Neural Networks for Determining Values of Selected Strength Parameters of Miscanthus × Giganteus," Sustainability, MDPI, vol. 14(5), pages 1-26, March.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:5:p:3062-:d:765192
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