Parametric Studies of Unbalanced Snow Loads on Arched Roofs
Bibliographic record
Abstract
Building codes and standards often indicate that unbalanced loading of arched roofs need not be considered if the roof is flat enough. They typically provide a threshold value for arch height as the criterion. However, real snow doesn't behave differently slightly above the threshold than slightly below. Recent building collapses have brought this issue into discussion since buildings designed below the threshold still produced significant unbalanced loads. The purpose of this research project was to investigate the lower bound of arched roof geometry and to compare the results with provisions of the 1995 National Building Code of Canada (NBCC) and the American Society of Civil Engineers Standard (ASCE 7-02). Parametric Finite Area Element (FAE) snow loading simulations were performed on a range of arched roof geometries using a range of meteorological data sets. The results indicated that, in certain situations, significant unbalanced loading could be expected for roofs that would otherwise be treated as flat, warranting special consideration in the appropriate codes and standards. The study also indicated that the azimuthal orientation of the roof relative to the prevailing winds plays a major role in some cases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".