Comparative study on the gust load factor in the load codes and standards of five countries
Bibliographic record
Abstract
A comparison was made between the loading code for building structure of several countries: the Chinese code(GB50009-2001),USA code(ASCE7-98),Japanese code(RLB-AIJ1993),Canadian code(NBC1990),and Australian code(AS1170.2).In the first section,the gust load factor(GLF) method proposed by Davenport and used in major international codes and standards was summarized.In the second section,a comparison was made among several main parameters affecting GLF,including the mean wind speed(pressure) profile,turbulence intensity,and wind speed spectrum.(ASCE7-98),(RLB-AIJ2004),(NBC1990),and(AS1170.2) considered GLF as a constant,equal to the displacement gust response coefficient;the gust response coefficient in Chinese codes and standards referred to a quantity changing with height,equal to the inertial force gust response coefficient.The result shows that since USA codes and standards(ASCE7) select an average interval of 3s and other countries' codes and standards select longer average intervals,the American gust load factor is smaller than in other countries.Australian codes and standards(AS1170.2) consider similar second order fluctuating wind pressure.Canadian codes and standards(NBC) have larger turbulence intensity than other countries',making their wind spectral coefficient larger,and their background factor and resonance factor smaller than other countries.Japanese codes and standards(RLB-AIJ) have the smallest turbulence intensity,which leads to the biggest size reduction factor and the smallest gust load factor.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".