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Exposure Categories and Transitions for Design Wind Loads

2006· article· en· W2060316211 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Structural Engineering · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsRowan Williams Davies & Irwin (Canada)
Fundersnot available
KeywordsPlanetary boundary layerSurface finishBoundary (topology)Roughness lengthSurface roughnessBoundary layerWind profile power lawMeteorologyMathematicsWind speedEnvironmental scienceMechanicsMathematical analysisPhysicsEngineeringMechanical engineeringThermodynamics

Abstract

fetched live from OpenAlex

One of the greatest sources of uncertainty in the calculation of wind loads occurs in the selection of the wind exposure. This paper compares the traditional power-law exposure–coefficient curves used in North American codes and standards such as ASCE 7-02, 2002, with curves derived from modern models of the planetary boundary. It is concluded that the traditional exposure coefficients are reasonably consistent with modern boundary layer theory for heights below about 300m. Above that height the traditional exposure coefficients increasingly depart from modern theory. The paper also examines the relationship between the dimensions and density of ground roughness obstacles and the exposure coefficient. This relationship enables a more objective assessment to be made than by the typical method of judging exposure “by eye,” at least where the ground roughness is fairly uniform. However, many sites are affected by upwind changes in ground roughness. Simple expressions, suitable for use in a building code or standard, are proposed for calculating the effect of an upwind change in roughness on the exposure coefficient.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.918
Threshold uncertainty score0.215

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 head0.006
GPT teacher head0.181
Teacher spread0.176 · 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