Field Monitoring of Vertical Movement in a Six-Story Wood-Frame Building in Coastal British Columbia
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".