Measurement and analysis mechanical properties of cross-laminated timber (CLT) product: Case study on typical lampung lamina arrangement
Bibliographic record
Abstract
Recently, the development of cross-laminated timber (CLT) products that can substitute large-dimensional wood for structures and reduce waste of wood products with small dimensions and pieces continues to be developed, and demand from industries continues to increase. Therefore, this study aims to measure and analyze the mechanical properties of cross-laminated timber (CLT) products designed with a typical arrangement of the Lampung region, Indonesia, called a screen arrangement. Measurement and analysis of mechanical properties include measuring load tests and analyzing moments and stiffness of CLT products intended for floor and beam structures. The screen-type wood arrangement used in the product consists of an inner screen arrangement, an outer screen arrangement, and a standing screen arrangement with polyvinyl acetate adhesive. Load test measurements were carried out using a universal testing machine (UTM) and moment and bending stress analysis using engineering mechanics. Generally, the results show that CLT products with standing screens have smaller mechanical properties than those intended for floors and beams. This research shows that the typical CLT arrangement of the Lampung region (Indonesia), in the form of a screen, has strong mechanical properties and characteristics that can be compared with other arrangements.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".