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Simplified Hazard Modeling and Structural Reliability Analysis Considering Non-Synoptic Wind Systems (NSWS) in Canada

2020· book-chapter· en· W3093273408 on OpenAlexaffabout
Han Hong, Qian Huang

Bibliographic record

VenueOxford University Press eBooks · 2020
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsTornadoWind speedMeteorologyProbabilistic logicHazardEnvironmental scienceReliability (semiconductor)Wind engineeringEngineeringGeographyStatisticsMathematics

Abstract

fetched live from OpenAlex

Abstract High-intensity wind events such as tornadoes and downbursts can be very destructive to structures and infrastructure systems. In the present chapter, an overview of the assessment of the wind hazard due to tornadoes and downbursts for Canadian sites is provided. Available tornado occurrence information available in Canada that can be used as the basis to develop a tornado occurrence model is discussed. The chapter presents an overall framework to develop tornado wind-velocity hazard maps for Canada. It also explores the use of simple equivalent along height wind profile that could be used to evaluate tornadic wind loading for line-like structures and of a practical procedure to evaluate the failure probability of structures subjected to high-intensity wind events. It is indicated that for a certain class of prismatic structures, the use of nonlinear static pushover analysis can be adequate to evaluate the capacity curve of the structure subjected to downburst wind loading. A probabilistic model of the capacity curve obtained in such a manner can then be used to evaluate the structural reliability by incorporating the assessed wind-velocity hazard map and equivalent wind profile.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.596
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.015
GPT teacher head0.174
Teacher spread0.159 · 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.

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 routes2
Has abstractyes

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