STR-940: PARAMETRIC SIMULATION OF ROOF STRUCTURAL SNOW LOADS
Bibliographic record
Abstract
While the National Building Code of Canada (NBCC) provides engineers with suitable snow loading guidelines for structural design, the strict application of the code may not lead to an optimized structural design. Generalizations have been made to ensure the applicability of the code to the majority of potential structures within Canada, which result in conservative estimates in certain situations. In particular, the interaction between region-specific prevailing wind directionality, climate and roof orientation are not accounted for. However, the development of advanced physical and numerical snow simulation approaches allows for the investigation of building-specific variables that affect snow loading. The Finite Area Element (FAE) process simulates the hour-by-hour accumulation and depletion of snow on a specific building design. This tool provides detailed quantification of the probabilistic snow loading accounting for region-specific long term meteorological conditions and building-specific variables such as roof size, exposure to prevailing winds, thermal capacity and local aerodynamics. While providing a detailed assessment of the snow loads, a full FAE assessment can be both time consuming and relatively costly for many applications. This parametric analysis approach has been developed using a variety of simple building geometries to provide an approach to assess the relative impacts of many of the key variables needed to inform a design. This paper describes the physical and numerical models used for the parametric simulation of snow loads, and discusses their application to structures within Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".