Modelling of creep behaviour of timber dowelled beams
Bibliographic record
Abstract
Extensive researches have been dedicated to study creep in wooden structures where good insight was gained on the phenomenological behaviour. However, long-term creep laws and practical methods to investigate the creep’s influence on safety and serviceability of wood structures during their life-cycle are still very few or don’t exist. This paper investigates the creep of wooden structures using a combined numerical and experimental study. Uni-axial tests (i.e. compression/tensile tests in different directions) were used to calibrate the constitutive law of spruce species and to identify the elastic parameters of the constitutive law. For creep parameters identification, three point bending tests were performed for compressed and uncompressed spruce wood. Subsequently, the results of the three point bending tests were used in conjunction with the numerical model in an inverse problem to obtain the viscous parameters. This technique was adopted for both compressed and uncompressed wood samples. Furthermore, a parametric study was conducted on laminated beams of uncompressed spruce boards assembled by compressed spruce dowels. The behaviour of the whole hybrid structure was studied and several interesting findings were highlighted.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".