Prioritizing Indicators for Material Selection in Prefabricated Wooden Construction
Bibliographic record
Abstract
Material selection in buildings profoundly affects project success, encompassing durability, maintenance, customer satisfaction, production systems, lifecycle, usage, environment, and costs. Yet, there is a need for further research on indicators for choosing materials in prefabricated buildings. Therefore, this study’s main objective was to identify the indicators (criteria and sub-criteria) for selecting materials for prefabricated wooden construction and, subsequently, categorize these criteria and sub-criteria based on the perspective of industry professionals. To achieve this goal, three phases were carried out. First, a literature review was conducted to identify potential criteria for choosing structural and envelope materials in wooden prefabricated buildings. Second, a pilot survey was conducted in Canada and the United States to classify the priority order of the criteria obtained from the literature based on professionals’ opinions. Finally, Monte Carlo simulations were conducted with different iterations (1000, 10,000, and 100,000) using the data obtained from the previous phase to improve decision-making and classification processes. For the indicators to select materials, the literature review identified seven main criteria: performance properties, green materials, energy efficiency, circular economy, site conditions and material logistics, standards, and social impact. These criteria contained a total of 25 sub-criteria. The pilot survey data analysis demonstrated that the performance properties, site conditions and material logistics, and social impact criteria were consistently prioritized. The critical sub-criteria identified were fire resistance, watertightness, local availability, occupant health, and safety and protection. For the Monte Calo simulations, the predictions aligned with the pilot study, enhancing the robustness of the results.
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".