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Record W4235985871 · doi:10.1520/jte20170247

Field Monitoring of Vertical Movement in a Six-Story Wood-Frame Building in Coastal British Columbia

2018· article· en· W4235985871 on OpenAlexaffabout
Jieying Wang

Bibliographic record

VenueJournal of Testing and Evaluation · 2018
Typearticle
Languageen
FieldEngineering
TopicStructural Engineering and Vibration Analysis
Canadian institutionsFPInnovations
Fundersnot available
KeywordsRoofTrussGeologyShrinkageVertical displacementGeotechnical engineeringStructural engineeringEnvironmental scienceEngineeringMaterials science

Abstract

fetched live from OpenAlex

Abstract Vertical movement was monitored for 24 months in a six-story wood-frame residential building in the coastal climate of British Columbia, Canada, from construction to service. The work was part of a long-term study to assist in the design of mid-rise wood-frame buildings. Displacement sensors were installed from the first floor to the top floor in a party wall, a hallway wall, and an interior partition wall, plus in the bottom two floors of an exterior wall to measure vertical movement, after the roof sheathing was installed. In addition, sensors were installed in the party wall and the exterior wall on the first floor to measure the moisture content of the wood, together with sensors for measuring environmental conditions in service. It was found that downward vertical movement, i.e., building shortening, occurred from construction to service and leveled off after a period of about 17 months. From the top of sill plates to the underside of roof trusses, the shortening reached approximately 34 mm at the party wall, 35 mm at the hallway wall, and 37 mm at the interior partition wall. The average shortening amount of 35 mm exceeded the predicted shrinkage amount based on a commonly used calculation method by about 25 %. The effects of loads on vertical movement should be taken into account in the design of mid-rise wood-frame construction.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.307
Threshold uncertainty score0.227

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.024
GPT teacher head0.277
Teacher spread0.253 · 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 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

Citations0
Published2018
Admission routes2
Has abstractyes

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