Prevention of Disproportionate Collapse for Multistory Mass Timber Buildings: Review of Current Practices and Recent Research
Bibliographic record
Abstract
The expanding height of multistory mass timber buildings makes the development of guidance to prevent disproportionate collapse an increasingly important area of engineering design; however, there is no guidance available in building codes or design standards on how to prevent disproportionate collapse of multistory mass timber buildings. In this paper, practical project-specific solutions applied in constructed prominent multistory mass timber buildings to prevent disproportionate collapse are presented. Various approaches have been successfully used for different construction types of multistory mass timber buildings to decrease the potential risk of disproportionate collapse, such as using walls above supports as deep beams and using columns to carry tension forces and hold the floor below in case a supporting column or wall is damaged. Then, recent and ongoing research in the field of prevention of disproportionate collapse of multistory timber buildings is reviewed. Such research is mostly based on numerical simulations of element removal scenarios and pushdown experiments on floor systems and connections and provides valuable insights and guidance to designers on important structural aspects regarding the prevention of disproportionate collapse in multistory mass timber buildings. Finally, the draft provisions for an upcoming standard related to the prevention of disproportionate collapse are reviewed, and the conclusions regarding the state of the art are presented.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".