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Record W7162459073 · doi:10.65521/ijasret.v9i6.1564

Dynamic Wind Analysis of Different Shapes Tall Building

2025· article· W7162459073 on OpenAlexaff
Harshit Verma, Sumit Pahwa

Bibliographic record

VenueInternational Journal of Advance Scientific Research and Engineering Trends · 2025
Typearticle
Language
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsPrairie Improvement Network
Fundersnot available
KeywordsTurbulenceWind engineeringAirflowVortex sheddingWind forceWind directionWind gradientVortex

Abstract

fetched live from OpenAlex

In this we are study G+50 stories tall building with different shapes, Shapes can influence wind interacts with the structure. By different studies we already know buildings with sharp edges or irregular geometries tend to create more turbulence and, thus, experience higher dynamic loads than those with streamlined, cylindrical, or uniform shapes. Wind loads are modelled based on wind speed, direction, turbulence intensity, and other atmospheric conditions. These factors are combined to predict the wind's impact on a building at various heights. Different building shapes have different responses to wind. Tall buildings with tapered or rounded shapes generally experience lower wind loads due to better airflow around them, while those with sharp angles or corners may lead to vortex shedding and higher wind-induced forces.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.671
Threshold uncertainty score0.694

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.014
GPT teacher head0.325
Teacher spread0.311 · 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 designSimulation or modeling
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

Citations0
Published2025
Admission routes1
Has abstractyes

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