Repozytorium Instytucjonalne
Akademii Nauk Stosowanych
w Nowym Sączu
dc.contributor.author | Francik, Sławomir | |
dc.contributor.author | Łapczyńska-Kordon, Bogusława | |
dc.contributor.author | Pedryc, Norbert | |
dc.contributor.author | Szewczyk, Wojciech | |
dc.contributor.author | Francik, Renata | |
dc.contributor.author | Ślipek, Zbigniew | |
dc.date.accessioned | 2023-11-10T08:38:25Z | |
dc.date.available | 2023-11-10T08:38:25Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Francik S, Łapczyńska-Kordon B, Pedryc N, Szewczyk W, Francik R, Ślipek Z. The Use of Artificial Neural Networks for Determining Values of Selected Strength Parameters of Miscanthus × Giganteus. Sustainability. 2022; 14(5):3062. https://doi.org/10.3390/su14053062 | pl_PL |
dc.identifier.issn | 2071-1050 | |
dc.identifier.uri | http://195.117.226.27:8080/xmlui/handle/123456789/516 | |
dc.description.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. | pl_PL |
dc.language.iso | en | pl_PL |
dc.publisher | MDPI | pl_PL |
dc.rights | Attribution 4.0 International | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.subject | biomechanical parameters | pl_PL |
dc.subject | miscanthus stem | pl_PL |
dc.subject | modulus of elasticity | pl_PL |
dc.subject | maximum stress | pl_PL |
dc.subject | bending test | pl_PL |
dc.subject | multilayer perceptron | pl_PL |
dc.title | The Use of Artificial Neural Networks for Determining Values of Selected Strength Parameters of Miscanthus × Giganteus | pl_PL |
dc.type | Article | pl_PL |