Disproportionate Collapse Mitigation in Tall Mass Timber Buildings
Bibliographic record
Abstract
Tall buildings are a unique type of structure with their own characteristic behaviour. They are most often occupied by a large number of people; therefore, their damage, loss of functionality, or, in worst case scenario, collapse will lead to catastrophic consequences. There are methodologies intended to provide structural integrity or increase structural robustness in tall buildings, thereby making structures resistant to disproportionate collapse, which is characterized by a cascading progression of damage that is not proportionate to the initial failure. Tall buildings are commonly constructed with steel and concrete. As a result, most of the attempts at providing structural integrity are dedicated to mitigating the effect of disproportionate collapse in the steel and concrete members, connections, and their systems. On the other hand, with rising demand for new sustainable buildings in urban areas, tall mass timber buildings have attracted increased attention nationally and internationally. Ease of modularization and offsite construction is one of the greatest advantages of using mass timber in tall building construction in the congested urban areas of major cities. A major challenge facing the engineering community is the lack of research studies regarding the structural robustness required to mitigate the potential of disproportionate collapse. The current study seeks to begin the process of understanding the behaviour of mass timber components and assemblages, and make recommendations regarding their performance and possible means to mitigate the occurrence of disproportionate collapse. These recommendations would lead to safer structural performance in the event of localized damage that has the potential to spread to a disproportionately large part of the structure.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".