Assessment of overstrength factor for seismic design of cross laminated timber structures: research and experimental investigation
Bibliographic record
Abstract
In the perspective of seismic engineering the adoption of Capacity Design principles requires that ductile failure mechanism take place before the failure of brittle members. This work investigates the causes and implications of the hidden reserve of strength that could compromise this behaviour for Cross Laminated Timber structures. In the first chapter an introduction to the basic concepts behind Capacity Design philosophy and how these apply to timber buildings is presented. Furthermore, an overview on how the Capacity Design principles are treated by the building codes of Europe, Canada and New Zealand is presented and discussed. In the second chapter, the methods and results on how the overstrength factor has been so far calculated in the literature for timber building are reported and discussed. A comparison has also been made between the techniques used to evaluate the overstrength factor for other common building materials and timber. The third chapter treats the planning and the execution of the experimental tests performed in the university’s laboratory. After a description of the material, equipment and methods used for the tests, the outcomes of the investigation are presented and discussed.\nFinally, in the concluding chapter the implications of the results are critically discussed and a suggestion on how to assume the overstrength factor is presented. Moreover, some suggestion on how future research could further investigate the matter are also given.
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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) | 0.000 | 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 | 0.001 | 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".