Analytical methods for comparative evaluation of bending behaviour of simply-supported and pinned wood beams
Bibliographic record
Abstract
Current design standards for wood-based beams face two major limitations: (1) inadequate material characterisation of the asymmetry between compression and tension, and (2) insufficient consideration for catenary action caused by horizontal constraints. The aim of this study was to propose a simplified analytical method used for the beam in which both the compression – tension material discrepancy and the catenary action were incorporated. Initially, the influence mechanism of horizontal constraints on the wood beam bending behaviour was numerically analysed. Then, the simplified analytical methods were derived based on the mechanism. Finally, these methods were used to quantitatively evaluate the influence of the horizontal constraints and compressive-to-tensile strength ratio (ρ) on beam performance. The proposed methods accurately captured both asymmetric tension – compression behaviour and catenary action, achieving less than 10.6% deviation from numerical simulations. Based on the proposed analytical method, the performance of wood beams can be evaluated efficiently and comprehensively. The findings indicate that beams with a lower ρ exhibit greater ductility and are more suitable for resisting extreme loads. The results also demonstrates that the capacity modification factor (γRE = 0.8) recommended in current seismic codes is non-conservative for wood with ρ > 0.77. This study facilitates the application of wood beams.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".