Numerical modelling of the structural response of a novel hybrid densified wood filled-aluminium tube dowel for structural timber connections
Bibliographic record
Abstract
This paper deals with the finite element modelling of the structural response of timber connections assembled using a novel hybrid dowel, made of densified wood filled aluminium tube, for the first time. Predictive and comprehensive finite element models, using the LS-Dyna Software, are developed to thoroughly investigate the non-linear 3D mechanical behaviour and optimize the design of the aluminium-to-timber connections. The materials parameters of the models are, first, identified based on experimental data from three-point bending tests. Then the models are validated by comparison to experimental data from slotted-in aluminium plate timber connections assembled either using steel dowel or hybrid densified wood filled aluminium tube dowel. The results are compared in terms of load-slip curves as well as in terms of failure modes. In addition, a parametrical study is conducted using the validated finite element model to investigate the influence of some material and geometrical parameters. The results showed that the hybrid densified wood filled aluminium tube dowels can be a potential substitute for conventional steel dowels. The study highlights the efficiency of the proposed finite element model in terms of quality of results and efficiency of use upstream the design process of such structures.
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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 | 0.001 | 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".