Artificial Neural Network Prediction of Mechanical Properties in Mycelium-Based Biocomposites
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41210%2F25%3A102041" target="_blank" >RIV/60460709:41210/25:102041 - isvavai.cz</a>
Alternative codes found
RIV/60460709:41320/25:102041
Result on the web
<a href="https://www.mdpi.com/2073-4360/17/18/2506" target="_blank" >https://www.mdpi.com/2073-4360/17/18/2506</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/polym17182506" target="_blank" >10.3390/polym17182506</a>
Alternative languages
Result language
angličtina
Original language name
Artificial Neural Network Prediction of Mechanical Properties in Mycelium-Based Biocomposites
Original language description
Mycelium-based biocomposites (MBBs) represent a sustainable alternative to synthetic composites, as they are produced from lignocellulosic substrates bonded by fungal mycelium. Their mechanical performance depends on multiple interacting factors, including the substrate composition, fungal species, and processing conditions, which makes property optimisation challenging. In this study, an artificial neural network (ANN) model was developed to predict two mechanical properties of MBBs, namely internal bonding (IB) and compressive strength (CS). An ANN model was trained on experimental data, using the substrate composition, fungal species, and physical properties of MBBs. The ANN predictions were compared with measured values, and the model accuracy was evaluated. The results showed that the ANN achieved a high predictive accuracy, with coefficients of determination of 0.992 for IB and 0.979 for CS. IB values were predicted more precisely than CS, likely due to microstructural heterogeneities. The heterogeneities were visualised using scanning electron microscopy. Composites produced with Ganoderma sessile and Trametes versicolor exhibited the highest IB. Interestingly, Trametes versicolor achieved the highest CS on virgin wood particles but the lowest values on recycled wood, underlining the strong influence of the substrate quality. The study demonstrates that ANNs can effectively predict the mechanical properties, reducing the number of experimental tests needed for material characterisation.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20505 - Composites (including laminates, reinforced plastics, cermets, combined natural and synthetic fibre fabrics; filled composites)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
POLYMERS
ISSN
2073-4360
e-ISSN
2073-4360
Volume of the periodical
17
Issue of the periodical within the volume
18
Country of publishing house
CH - SWITZERLAND
Number of pages
16
Pages from-to
1-16
UT code for WoS article
001579907000001
EID of the result in the Scopus database
2-s2.0-105017371633