DESIGN FOR PONDING OF RAINWATER ON LARGE FLAT STEEL ROOFS
Bibliographic record
Abstract
Rainwater on a large flat roof ponds, intensifying the load at the most critical location to risk instability and collapse of the roof. The current specifications in the National Building Code of Canada are based on 35-year old research and the current state of knowledge is unsatisfactory. In particular, residual stresses that may cause premature yielding of steel members are not considered in the current design criteria. This research presents a ponding map of Canada based on the relative magnitudes of snow and rain load alone that identifies Vancouver Island, Calgary, southwestern Ontario and southern Nova Scotia as regions where flat roofs are particularly susceptible to ponding failures. It summarizes a study on the impact of residual stresses on the secant stiffness of W-shapes. An analytical tool was developed to investigate current ponding criteria considering the effects of residual stresses, beam camber, joist camber, rainfall intensity, and member stiffnesses (serviceability limit states) on the performance of a typical simple-span roof system in Calgary. Finally, Gerber roof systems, which have different deflection characteristics than simply supported systems, were investigated. An example iterative calculation is provided to illustrate ponding calculations for a typical Gerber system, whereby water from one span can flow towards the adjoining span potentially overloading the span and initiating a structural collapse.
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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 | 0.000 |
| 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 | 0.001 | 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".