DEEP LEARNING-BASED GEOMETRICAL ANALYSIS AND TENSIONSTRAIN MAPPING OF BAMBOO FIBRE MICROSTRUCTURE [* Cross-Reference *]

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Cardona, Carlos; Garcia, Lina; Fuentes, Carlos, 2026, "DEEP LEARNING-BASED GEOMETRICAL ANALYSIS AND TENSIONSTRAIN MAPPING OF BAMBOO FIBRE MICROSTRUCTURE [* Cross-Reference *]", https://lore.list.lu/citation?persistentId=perma:LIST.SZRCBJ, LIST Open Repository, V1
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Description
Bamboo technical fibre is a structurally graded natural composite material consisting of tubularelementary fibres bonded by a middle lamella. Its hierarchical design exhibits localized distortions ofunknown configuration. We applied a deep learning-based geometrical analysis to extract the spatialdistribution of fibre hierarchies in the cross-section, correlating this with tensile strain maps in thelongitudinal direction. The lumen fraction, representing the percentage of the total technical fibre cross-sectional area occupied by lumens, averaged 7 to 13%. In the middle part of the cross-section, the middlelamella had an average thickness of 0.48 μm, while the elementary fibres averaged 13.79 μm in diameterand their lumens averaged 2.7 μm in diameter. Using vector fields derived from gradient cubicapproximations, we evaluated the positional distribution of elementary fibres on the cross-section andcontrasted this with micro-tensile strain maps. This analysis categorized fibre failure origins asasymmetric defibrillation and cell wall cracking, identifying key parameters for physics-informedmodels (2026-03-25)
***This entry has been automatically imported via Datacite by LIST harvest scripts. Please refer to https://doi.org/10.5281/zenodo.19219578 for the original and latest version of the dataset and data downloads*** (2026-04-14)
Subject Physics
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