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Record W2323667439 · doi:10.1061/40700(2004)55

Parametric Studies of Unbalanced Snow Loads on Arched Roofs

2004· article· en· W2323667439 on OpenAlexaffabout
Frank M. Hochstenbach, Peter Irwin, Scott Gamble

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsRowan Williams Davies & Irwin (Canada)
Fundersnot available
KeywordsRoofParametric statisticsSnowBuilding codeStructural engineeringArchRange (aeronautics)Finite element methodFlat roofGeotechnical engineeringEngineeringEnvironmental scienceMeteorologyGeographyMathematicsStatistics

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.237
Threshold uncertainty score0.484

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.281
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2004
Admission routes2
Has abstractyes

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