A Comparative Analysis of Design Criteria Influencing Building Material Selection Across Different Architectural Contexts
Bibliographic record
Abstract
The process of selecting building materials is a complex process that is affected by many restrictions, criteria and considerations.Often, the process is carried out spontaneously without considering the design criteria and neglects the building's function.Therefore, it is crucial to identify the key criteria impacting material selection.Design criteria vary based on a building's intended purpose and location, leading to distinct considerations.This study identifies five main design criteria: physical, cultural-social, environmental, economic, and technical.Experts in architecture participated in a survey, with Analytic Hierarchy Process (AHP) used to assign weights to these criteria.The research findings highlight that material select for a building's envelope depend on its function and context.In religious buildings, cultural criteria are vital, regardless of historical or modern contexts.Historical residential buildings prioritize cultural criteria, while modern ones lean toward economic considerations.Commercial buildings have historically been influenced by physical factors but are now more influenced by technical criteria.This study highlights the importance of considering a variety of design criteria when selecting building materials to ensure effective adaptation to the building's use and context.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".