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Lateral Resistance of Sheathing-to-Framing Nailed Joints with an Intermediate Insulation Layer

2021· article· en· W3151142013 on OpenAlex
Marko Spasojevic, Hossein Daneshvar, Yuxiang Chen, Ying Hei Chui

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Structural Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsNatural Sciences and Engineering Research Council of CanadaUniversity of Alberta
Fundersnot available
KeywordsEmbedmentFraming (construction)Structural engineeringShear wallMaterials scienceComposite materialEngineering

Abstract

fetched live from OpenAlex

Lateral resistance of shear walls constructed with wood-based sheathing panels and lumber framing is largely governed by the strength of the sheathing-to-framing nailed joints. In new wall designs, a layer of soft thermal insulation is added between the sheathing and framing to increase the wall thermal resistance. Studying the influence of the intermediate insulation on the lateral load resistance of the nailed joint is essential for understanding the structural behavior of this kind of shear walls. An experimental study was conducted to measure the lateral resistance of the nailed joints. The results show that the insulation has a significant impact on the lateral resistance. An analytical model for predicting the lateral resistance, with embedment strengths of sheathing and framing members and nail bending strength as input parameters, was developed. The model predictions were compared with those from existing analytical models and with the experimental data. The ultimate lateral resistance of the nailed joints can be predicted with sufficient accuracy using the developed analytical model for the joints with up to 51-mm-thick intermediate insulation layers.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.363
Threshold uncertainty score0.746

Codex and Gemma teacher scores by category

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

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 head0.006
GPT teacher head0.209
Teacher spread0.203 · 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