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

Sławomir Francik, Bogusława Łapczyńska-Kordon, Norbert Pedryc, Wojciech Szewczyk, Renata Francik and Zbigniew Ślipek
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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
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

Sustainability, 2022, vol. 14, issue 5, 1-26

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.

Keywords: biomechanical parameters; miscanthus stem; modulus of elasticity; maximum stress; bending test; multilayer perceptron (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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