Wind Impacts on Buildings and Structures in the Republic Of Guinea
Bibliographic record
Abstract
Currently in the Republic of Guinea, high-rise buildings and structures construction projects are emerging in high rate.However, there is a lacks in codes of standard that take into account the wind parameters that is essential for the successful completion of a construction project.One of the major climatic factor is a wind load that is not accurately considered in calculating when designing buildings and structures in Guinea.The purpose of this study is to determine the estimated wind pressure for the whole territory of Guinea.The raw data for the last 25 years of wind speed were obtained from the databases of the National Directorate of Meteorology for 4 (four) meteorological stations.Then, based on the linear regression law, we were able to determine the probable values of the mean wind speed from the data of each meteorological station with a return period of 50 years.For areas of Guinea located outside the vicinity (200 km radius) of the meteorological stations, the values of mean wind speed were calculated using the OK (Ordinary Kriging) method.As a result, we were able to: -map the zoning of the Guinean territory according to the baseline wind pressure; -determine the coefficients that take into account the influence of altitude and roughness of the different types of terrain; -calculate the parameters that take into account wind turbulence.It assumed that the obtained results will help to minimize the risks of collapse of buildings and structures under construction in the considered meteorological zones by considering the wind load in the design of building structures.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".