Verification and Validation of finite element models for laminated timber structures using solid, solid-beam and solid-shell approaches
Bibliographic record
Abstract
This paper presents a numerical approach to assess efficiently the vibration performance of adhesive-free engineered wood products assembled through compressed wood dowels. Predictive finite element models are obtained by applying the Verification and Validation methodology. The models are first developed using solid elements. Then, solid-beam and solid-shell approaches based on standard solid elements are developed with first-order or higher-order theories. Beam or shell kinematic assumptions are applied throughout the cross-section of the dowels and through the thickness of the layers. From a numerical point of view, a modification of the system of algebraic equations, based on the concept of independent and dependent nodes, is developed. Dependent nodes are eliminated, resulting in a significant reduction in the number of degrees of freedom and floating-point operations. The methodology is evaluated for the calculation of frequencies and mode shapes of adhesive-free laminated timber beams and timber panels with a mix of solid elements, solid-shell and solid-beam approaches. The study highlights the efficiency of the proposed modelling approach in terms of quality of results and model size reduction.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".