Seismic and progressive collapse strain rate effects on the compressive, tensile and shear mechanical properties of softwood Laminated Veneer Lumber (LVL)
Bibliographic record
Abstract
This study experimentally investigates the strain rate effects encountered during seismic and progressive collapse events on the parallel- and perpendicular-to-grain compressive and tensile mechanical properties, as well as edge shear properties, of softwood Laminated Veneer Lumber (LVL). Specifically, it examines (1) the compression Modulus of Elasticity (MOE), strength, and ductility, (2) the tension and shear strengths of the material. Tests were performed to induce failure between 200 s (quasi-static) and 0.2 s (short-term loading). In total 360 tests were performed. The material was found to be sensitive to the range of strain rates investigated, though the degree of sensitivity varied depending on the type of loading. Between the two extreme strain rates, the parallel-to-grain compression and edge shear strengths increased by 14.6 %, and 17.8 %, respectively, but the perpendicular-to-grain compression ductility decreased by 24.2 % under short-term loading. The latter result may have significant implications in the design of connections and energy absorption during seismic events. The MOE and the tensile strength, both parallel- and perpendicular-to-grain, remained largely stable, showing no significant differences across strain rates. The study indicates that the one-size-fits-all short-term load duration factors used in international standards may need to be tailored to the specific failure mode.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".