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Record W2566723104 · doi:10.1139/cjce-2015-0418

Damage level assessment of response limits in light-frame wood stud walls subjected to blast loading

2016· article· en· W2566723104 on OpenAlexaffvenue
Christian Viau, Daniel Lacroix, Ghasan Doudak

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicStructural Response to Dynamic Loads
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsStructural engineeringDuctility (Earth science)Frame (networking)Impulse (physics)Materials scienceEngineeringMechanical engineeringComposite material

Abstract

fetched live from OpenAlex

Currently, no systematic approach exists for damage evaluation of light-frame wood structures subjected to blast loading. This paper presents a detailed assessment of the behaviour of 33 full-scale light-frame wood stud walls subjected to a total of 48 shots of simulated blast loading. Detailed documentation of the observed damage allowed for the development of an accurate evaluation strategy of the response limits. The observed response limits are compared to limits derived from single-degree-of-freedom modelling using scaled pressure–impulse diagrams and to current code performance levels. It was concluded that the assumption made in contemporary blast design codes overestimates the ductility ratios for light-frame wood stud walls, and that using a maximum ductility of 2 is more appropriate and safer for blast design. Based on the observed damage levels obtained from the experimental study, the authors propose new ductility ratios corresponding to four damage regions.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.227
Teacher spread0.215 · 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

Citations19
Published2016
Admission routes2
Has abstractyes

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