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Record W2334007339 · doi:10.1061/41130(369)110

Wind Induced Fatigue of Metal Roof Cladding during Severe Tropical Cyclones

2010· article· en· W2334007339 on OpenAlexaff
David Henderson, John Ginger, Gregory A. Kopp

Bibliographic record

VenueStructures Congress 2010 · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsCladding (metalworking)Structural engineeringWind speedWind engineeringEnvironmental scienceCyclone (programming language)RoofGeologyEngineeringMaterials science

Abstract

fetched live from OpenAlex

The corner regions of roofs of low-rise buildings are subjected to large fluctuating pressures during windstorms. Damage investigations of metal cladding following hurricanes and cyclones in the US and Australia have shown loss of cladding due to wind induced fatigue cracking. To mitigate for low cycle fatigue failures, cladding systems are required to demonstrate compliance to dynamic wind loading test criteria such as the Low-High-Low (L-H-L) test detailed in the Building Code of Australia. The test criteria's loading sequences are a simplification of the dynamic wind loading environment, where assumptions such as building orientation, cyclone duration, wind direction and cycle range have been made. For example, when applying the Australian test criteria, the L-H-L test, the maximum negative external and maximum positive internal pressures are combined and applied across the load cycles. However, when analysing external and net pressure cycles for various windward and side-wall dominant opening cases, the numbers of cycles vary along with the mean and range, indicating a potentially conservative test criteria. An advanced pressure loading actuator (PLA) developed by UWO, is used to apply the fluctuating pressures of a "design" cyclone to sections of roof cladding. The PLAs permit the application of actual temporally varying wind pressures to a representative test section of the building envelope. The test method is able to replicate cladding failures as observed following severe cyclones and demonstrate conservative aspects of the L-H-L test criteria.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.013
GPT teacher head0.242
Teacher spread0.228 · 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 designObservational
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

Citations3
Published2010
Admission routes1
Has abstractyes

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