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Record W6893241663 · doi:10.5281/zenodo.15777713

Application of Intensifying Wind Analysis in Time History Analysis of Tall Buildings

2020· dissertation· en· W6893241663 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2020
Typedissertation
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsWind powerWind speedWind engineeringReturn periodPaceProcess (computing)

Abstract

fetched live from OpenAlex

Abstract The wind phenomenon has always caused significant damage to structures. Suffering from extensive damage and loss over the years has led researchers to establish the novel science of wind engineering. Wind engineering references introduce wind as an intricate dynamic and random phenomenon and encourage engineers to familiarize themselves with its exact effects on structures by utilizing the latest methods of structural analysis, such as time history analysis. In this research, the idea of a new method for time history analysis of tall buildings against the wind is proposed. In this method, the purpose is to determine the responses of the structure against any wind of any intensity and return period by performing a single analysis. The purpose is achieved by generating and utilizing some special functions called intensifying wind records. The aforementioned process paves the way for a comprehensive and accurate assessment of the structure, and for performing the efficient performance-based design which is developing at a galloping pace in recent years. In order to perform the wind time history analysis, it is indispensable to generate and assign wind speed records to the structure. After performing a time-consuming process of artificially generating and assigning unique wind records to each node of the tall structure in the software and performing a time history analysis under the simultaneous effect of all of them, the response of the structure subjected to a specific wind with a specified mean speed and return period will be obtained. It goes without saying that in the performance-based wind design for instance, determining the responses and behavior of the structure under the influence of various winds with different return periods is required. In this case, there is no other way but to repeat the whole illustrated process. In order to solve this problem, the present study assesses wind records through a creative viewpoint and by combining several complicated methods of wind record production obtains intensifying wind records, simplifies the difficult problem of record generation.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.224
Teacher spread0.207 · 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 source (direct Gemma or distilled Codex), 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
Published2020
Admission routes1
Has abstractyes

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