Contribution of Plasterboard Finishes to Structural Performance of Multi-storey Light Wood Frame Buildings
Bibliographic record
Abstract
The main objective of this paper is to investigate the contribution of plasterboard finishes made of gypsum wall board (GWB) to the structural performance of multi-storey light wood frame building (LWFB) subjected to earthquake load. Four- to six-storey buildings were analysed in this study. Computer software, SAPWood, developed to analyze LWFB subjected to actual earthquake motions was used. Two cases were considered in the analyses. The first one was a reference case where all shear walls are fabricated with wood-based sheathing panels only. The second case was buildings with walls fabricated with wood-based sheathing panels plus GWB. All shear wall hysteretic properties for both cases (with and without GWB) and inter-storey (hold-down) connections were derived from detailed numerical modeling of wall sub-systems available in the SAPWood database. The buildings were subjected to a major earthquake ground motion excitation, and the ground motion was scaled until failure in the components (walls or hold-down connections) or excessive inter-storey drift was reached. Main outputs that were used as comparison between the two cases included natural period, maximum storey shear force and drift, and individual wall responses (force and deformation). Specific attention was paid to how the applied forces are distributed between the different types of wall panels i.e. wood-based and gypsum-based.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".