Analytical Hierarchical Process as a Multicriteria Decision Tool in Material Selection for Prefabricated Wood Buildings
Bibliographic record
Abstract
The popularity of prefabricated wooden buildings is increasing in North America, but choosing suitable materials for these structures can be complicated. This can lead to problems like financial losses, production delays, and lower quality. Therefore, the main goal of this study was to use the Analytical Hierarchy Process (AHP) decision-making tool to rank the criteria for material selection for prefabricated wood buildings in Canada and the United States. The methodology involved surveys experts in the prefabricated wood construction industry from Canada and the United States. The data obtained from the questionnaires utilized for the AHP analysis were modeled using R programming language. The results revealed that for structural materials, the top five subcriteria were safety and security of building occupants (0.234), location, shape, and height of the building (0.218), comfort, satisfaction, and well-being of the building (0.155), occupant health (0.121), and availability of materials (0.098). For selecting envelope materials, the top five subcriteria were comfort, satisfaction, and well-being of the building (0.252), safety and security of building occupants (0.206), location, shape, and height of the building (0.178), occupant health (0.132), and availability of materials (0.078).
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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 | 0.019 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".