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Record W2955210593 · doi:10.29173/mocs124

Development of Rigging System for Prefabricated Wood I-joist Floor Panels

2019· article· en· W2955210593 on OpenAlexafffundvenue
Sigong Zhang, Ying Hei Chui, David Joo, Jean-Philippe Letarte, Luca Dalcastagne

Bibliographic record

VenueModular and Offsite Construction (MOC) Summit Proceedings · 2019
Typearticle
Languageen
FieldEngineering
TopicStructural Engineering and Vibration Analysis
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsJoistFlangeStructural engineeringEngineering

Abstract

fetched live from OpenAlex

Panelized building construction are highly mechanized. Material handling and lifting equipment dominate construction sites and constitute the critical element in achieving productivity. In recent construction practice, panelized wood I-joist floor panels are normally lifted into place by mobile crane using flexible slings inserted through the predrilled holes on the I-joist web and sheathing panels above the I-joist top flange and then wrapped around the I-joists at the four corners. However, the pre-drilled holes on the web and sheathing may weaken the floor panels. Moreover, a range of techniques for lifting and handling mass timber panels have been developed. A typical rigging technique consists of a lifting ring and a steel plate with pre-drilled holes. By using several self-tapping screws, the panel was connected with the rigging device for lifting. However, since prefabricated I-joist floor panels are much lighter than mass timber panels and the I-joist flange is relatively narrow and thin, the rigging device for mass timber panels cannot be applied directly to I-joist floor panels, but a modified design can be developed for prefabricated I-joist floor panels. In the present study, a new rigging device was designed for prefabricated wood I-joist panels and their load capacity was evaluated by withdrawal tests. Several factors influencing the withdrawal capacity were investigated including screw types and quantities, flange width and materials, and OSB thickness.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.560
Threshold uncertainty score0.854

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 head0.007
GPT teacher head0.184
Teacher spread0.177 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2019
Admission routes3
Has abstractyes

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