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Record W2130354100 · doi:10.1061/9780784412626.094

Analysis of Wood-Framed Roof Failures under Realistic Hurricane Wind Loads

2012· article· en· W2130354100 on OpenAlexaff
Gregory A. Kopp, Mohammad Khan, David Henderson, Murray J. Morrison

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsTrussRoofStructural engineeringStiffnessLoad sharingConnection (principal bundle)Slip (aerodynamics)Wind engineeringEngineeringComputer scienceAerospace engineering

Abstract

fetched live from OpenAlex

Typical North American wood frame houses have trusses that are toe-nailed to the wall top-plates. In order to develop models of roof failures for such structures, considering the effects of wind speed, direction and duration for a storm, as well as the structural details, knowledge of the load sharing and how it is altered by the nail slip at each of the connections is required. The objective of the current paper is to quantify the changes in load sharing during nail/connection slip. This is achieved by simplifying the roof structure to the essential components of a beam, which bends elastically in the direction normal to the trusses, and real, toe-nailed connections. Test results for a single "roof stiffness" and truss spacing show that significant load sharing is not observed until permanent displacements (i.e., damage) to a connection occur. Dynamic load tests, with the same time-varying load applied to each connection, have shown that the load sharing increases with the damage accumulation to each of the connections and changes continuously. Ultimately, this information will be used in the development of analytical models that are able to consider storm duration effects on the performance and failure of wood-frame houses in extreme winds.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.243
Teacher spread0.231 · 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 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

Citations2
Published2012
Admission routes1
Has abstractyes

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