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Predicting the strength of European beech (Fagus sylvatica L.) boards using image-based local fibre direction data
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/bate.199803690openalex-snapshot
World Conference on Timber Engineering
inferredcites →https://doi.org/10.1002/for.3980020210openalex-snapshot
Industrial and business forecasting methods, Lewis, C.D., Borough Green, Sevenoaks, Kent: Butterworth, 1982. Price: £9.25. Pages: 144
inferredcites →https://doi.org/10.1007/978-1-4757-3264-1openalex-snapshot
The Nature of Statistical Learning Theory
inferredcites →https://doi.org/10.1007/bf02607817openalex-snapshot
Einfluß keilgezinkter Lamellen auf die Biegefestigkeit von Brettschichtholzträgern
inferredcites →https://doi.org/10.1007/s00107-005-0076-6openalex-snapshot
Determination of fiber orientation in Norway spruce using X-ray diffraction and laser scattering
inferredcites →https://doi.org/10.1007/s00107-013-0684-5openalex-snapshot
Prediction of timber bending strength and in-member cross-sectional stiffness variation on the basis of local wood fibre orientation
inferredcites →https://doi.org/10.1007/s00107-014-0803-yopenalex-snapshot
Numerical simulation tool for wooden boards with a physically based approach to identify structural failure
inferredcites →https://doi.org/10.1007/s00107-015-0906-0openalex-snapshot
Microwave scanning as an additional grading principle for sawn timber
inferredcites →https://doi.org/10.1007/s00107-016-1034-1openalex-snapshot
Strength grading of sawn timber in Europe: an explanation for engineers and researchers
inferredcites →https://doi.org/10.1007/s00107-016-1049-7openalex-snapshot
Three-dimensional modelling of knots and pith location in Norway spruce boards using tracheid-effect scanning
inferredcites →https://doi.org/10.1007/s00107-016-1102-6openalex-snapshot
Strength grading on the basis of high resolution laser scanning and dynamic excitation: a full scale investigation of performance
inferredcites →https://doi.org/10.1007/s00107-016-1149-4openalex-snapshot
Modelling mechanical properties of spruce and Douglas fir timber by means of X-ray and grain angle measurements for strength grading purpose
inferredcites →https://doi.org/10.1007/s00107-017-1279-3openalex-snapshot
A non-contact method for the determination of fibre direction of European beech wood (Fagus sylvatica L.)
inferredcites →https://doi.org/10.1007/s00107-018-1286-zopenalex-snapshot
Comparison of three systems for automatic grain angle determination on European hardwood for construction use
inferredcites →https://doi.org/10.1007/s00107-019-01387-xopenalex-snapshot
Potential of microwave scanning for determining density and tension strength of four European hardwood species
inferredcites →https://doi.org/10.1007/s00226-005-0027-8openalex-snapshot
Knots detection in wood using microwaves
inferredcites →https://doi.org/10.1007/s00226-008-0189-2openalex-snapshot
Glued laminated timber in bending: new aspects concerning modelling
inferredcites →https://doi.org/10.1007/s00226-019-01144-6openalex-snapshot
Applying machine learning to predict the tensile shear strength of bonded beech wood as a function of the composition of polyurethane prepolymers and various pretreatments
inferredcites →https://doi.org/10.1007/s00226-020-01184-3openalex-snapshot
Prediction of mechanical properties of wood fiber insulation boards as a function of machine and process parameters by random forest
inferredcites →https://doi.org/10.1007/s002260100112openalex-snapshot
A three-dimensional paradigm of fiber orientation in timber
inferredcites →https://doi.org/10.1007/s10462-011-9272-4openalex-snapshot
Decision trees: a recent overview
inferredcites →https://doi.org/10.1007/s13595-018-0781-zopenalex-snapshot
Performance of strength grading methods based on fibre orientation and axial resonance frequency applied to Norway spruce (Picea abies L.), Douglas fir (Pseudotsuga menziesii (Mirb.) Franco) and European oak (Quercus petraea (Matt.) Liebl./Quercus robur L.)
inferredcites →https://doi.org/10.1016/j.conbuildmat.2018.06.050openalex-snapshot
An innovative method based on grain angle measurement to sort veneer and predict mechanical properties of beech laminated veneer lumber
inferredcites →https://doi.org/10.1016/j.conbuildmat.2019.01.032openalex-snapshot
Strength prediction of timber boards using 3D FE-analysis
inferredcites →https://doi.org/10.1016/j.conbuildmat.2020.120848openalex-snapshot
Prediction of the mechanical properties of wood using guided wave propagation and machine learning
inferredcites →https://doi.org/10.1016/s0143-8166(02)00144-6openalex-snapshot
Determination of wood grain direction from laser light scattering pattern
inferredcites →https://doi.org/10.1016/s0168-1699(03)00045-0openalex-snapshot
Automatic measurement of fiber orientation in softwoods by using the tracheid effect
inferredcites →https://doi.org/10.1038/nmeth.2019openalex-snapshot
Fiji: an open-source platform for biological-image analysis
inferredcites →https://doi.org/10.1051/forest/2010002openalex-snapshot
Influence of wood properties and bonding parameters on bond durability of European Beech (Fagus sylvatica L.) glulams
inferredcites →https://doi.org/10.1080/17480272.2014.951071openalex-snapshot
Improving strength grading of timber by grain angle measurement and mechanical modeling
inferredcites →https://doi.org/10.1098/rspa.1985.0034openalex-snapshot
The fracture and toughness of woods
inferredcites →https://doi.org/10.1108/03684920710743466openalex-snapshot
Pattern Recognition and Machine Learning
inferredcites →https://doi.org/10.1126/science.1127647openalex-snapshot
Reducing the Dimensionality of Data with Neural Networks
inferredcites →https://doi.org/10.1186/1471-2105-7-91openalex-snapshot
Bias in error estimation when using cross-validation for model selection
inferredcites →https://doi.org/10.1186/s10086-020-01922-yopenalex-snapshot
Influence of wood anatomy on fiber orientation measurement obtained by laser scanning on five European species
inferredcites →https://doi.org/10.1364/oe.23.033529openalex-snapshot
Automatic measurement of wood fiber orientation and knot detection using an optical system based on heating conduction
inferredcites →https://doi.org/10.1515/hf.2007.115openalex-snapshot
Neural network prediction of bending strength and stiffness in western hemlock (Tsuga heterophylla Raf.)
inferredcites →https://doi.org/10.15376/biores.13.3.5491-5503openalex-snapshot
Effect of grain direction on cutting forces and chip geometry during green beech wood machining
inferredcites →https://doi.org/10.15376/biores.3.2.437-451openalex-snapshot
Predicting the strength of sawn wood by tracheid laser scattering
inferredcites →https://doi.org/10.21611/qirt.1996.042openalex-snapshot
Thermal ellipsometry in steady-state or by lock-in thermography. Application for anisotropic materials characterization
inferredcites →https://doi.org/10.21611/qirt.2016.007openalex-snapshot
Wood fiber orientation assessment based on punctual laser beam excitation: A preliminary study
inferredcites →https://doi.org/10.3217/978-3-85125-314-6openalex-snapshot
Mechanische Kenngrößen von Buchen-, Eschen- und Robinienholz für lastabtragende Bauteile
inferredcites →https://doi.org/10.3929/ethz-a-010108864openalex-snapshot
Influence of varying material properties on the load-bearing capacity of glued laminated timber
inferredcites →https://doi.org/10.3929/ethz-b-000402805openalex-snapshot
European Beech Glued Laminated Timber
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.4760671openalex-extracted
Quantifying the Resistance of Mixed-Forest Against Natural Hazards in the Pyrenees
inferred← citeshttps://doi.org/10.2139/ssrn.4791418openalex-extracted
A Machine Learning-Based Model for Self-Shaping Wood Bilayers: Leveraging Local Natural Variations for Bending Prediction
inferred← citeshttps://doi.org/10.1007/978-3-032-17309-6_28openalex-snapshot
Optimization of Knots Assessment Using Laser Scattering for Predicting Beech Mechanical Properties
inferred← citeshttps://doi.org/10.1007/s00107-024-02055-5openalex-snapshot
Comparison of classical beam theory and finite element modelling of timber from fibre orientation data according to knot position and loading type
inferred← citeshttps://doi.org/10.1007/s00107-025-02333-wopenalex-snapshot
Experimental assessment of glulam beams made by European beech from Spain
inferred← citeshttps://doi.org/10.1007/s00226-022-01407-9openalex-snapshot
Semi-supervised learning for quality control of high-value wood products
inferred← citeshttps://doi.org/10.1007/s00226-024-01558-xopenalex-snapshot
Toward the modelling of laminated veneer lumber stiffness and the influence of the number of plies
inferred← citeshttps://doi.org/10.1007/s00226-024-01574-xopenalex-snapshot
On the char front temperature of beech (Fagus sylvatica)
inferred← citeshttps://doi.org/10.1007/s00226-024-01607-5openalex-snapshot
Determination of local mechanical properties of clear wood in relation to the local fiber deviation
inferred← citeshttps://doi.org/10.1016/j.aei.2025.104061openalex-snapshot
A machine learning based model for self-shaping wood bilayers: leveraging local natural variations for bending prediction
inferred← citeshttps://doi.org/10.1016/j.biortech.2022.126812openalex-snapshot
Machine learning prediction of lignin content in poplar with Raman spectroscopy
inferred← citeshttps://doi.org/10.1016/j.carbpol.2022.119635openalex-snapshot
Machine learning algorithms for rapid estimation of holocellulose content of poplar clones based on Raman spectroscopy
inferred← citeshttps://doi.org/10.1016/j.conbuildmat.2024.135448openalex-snapshot
Variability of mechanical properties of hardwood branches according to their position and inclination in the tree
inferred← citeshttps://doi.org/10.1016/j.conbuildmat.2025.141922openalex-snapshot
The potential of wood ray cells in beech (Fagus sylvatica) as a design parameter for veneer-based products
inferred← citeshttps://doi.org/10.1016/j.conbuildmat.2025.142359openalex-snapshot
Assessment of nailed connections in existing timber trusses
inferred← citeshttps://doi.org/10.1016/j.conbuildmat.2026.147198openalex-snapshot
An explainable deep learning approach based on the weakest-link principle — Application to oak timber tensile strength prediction
inferred← citeshttps://doi.org/10.1016/j.jobe.2026.117358openalex-snapshot
Cross-scale multimodal modeling of wood mechanical properties: Prediction and mechanistic insights
inferred← citeshttps://doi.org/10.1016/j.scitotenv.2024.174359openalex-snapshot
Quantifying the resistance of mixed-forests against natural hazards in the Pyrenees
inferred← citeshttps://doi.org/10.1021/acs.chemrev.2c00360openalex-snapshot
Sustainability in Wood Products: A New Perspective for Handling Natural Diversity
inferred← citeshttps://doi.org/10.1098/rsif.2023.0492openalex-snapshot
Probing the complexity of wood with computer vision: from pixels to properties
inferred← citeshttps://doi.org/10.1186/s10086-026-02266-9openalex-snapshot
Importance-driven bottleneck model: a multi-stage deep learning approach for analyzing swelling–shrinkage behavior of wood species and their mechanical properties
inferred← citeshttps://doi.org/10.3390/f14071419openalex-snapshot
The Utilization of European Beech Wood (Fagus sylvatica L.) in Europe
inferred← citeshttps://doi.org/10.3390/ma16186173openalex-snapshot
Investigations into the Influence of Temperature on the Tensile Shear Strength of Various Adhesives
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