Finite-Element Model for Wood-Based Floors with Lateral Reinforcements
Bibliographic record
Abstract
Lateral reinforcements can enhance serviceability of wood-based floor systems. However, because of a lack of proper analysis models, the benefit from installing these components has not been fully recognized in floor design. In this paper, a finite-element model for predicting static and dynamic characteristics of wood-based floor structures with various types of lateral reinforcements is presented. In this model, shell elements were employed to represent floor decking and ceiling, and beam elements were utilized to model joists and structural members in the lateral reinforcements. Special connector elements were developed to model the fasteners connecting various structural components. Other structural features, such as gaps perpendicular to joists in the subfloor, additional objects on floor and flexible supports, were also considered. A computer model incorporating these new features has been developed. For verification and validation purposes, the predicted deflections and natural frequencies for a number of full-size floors were compared with experimental values. Good agreement has been observed. These numerical test results indicated that the present finite-element model is reliable and unique, particularly in modeling floors with lateral reinforcements.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".