Evaluation of Building Materials Based on Sustainable Development Indicators
Bibliographic record
Abstract
Construction industry regarded as one of the key aspects of achieving the goals of sustainable development in communities. In this regard, the choice of building materials is one of the key challenges in order to improve project performance with respect to sustainable development indicators and the use of sustainable materials, is an effective step towards achieving sustainable construction. This research uses information and evidence, interview and questionnaire prepared (by five points Likert scale method). Also, it has provided expert opinions related indicators widely used in a construction materials, manufacturing process and defining the impact of the production of these materials on sustainable development deals. Validity and reliability of the questionnaires were also performed (with Cronbach's alpha method). As a result of this research, Cement was identified as the most unsustainable material, after that Steel and then Brick and Glass were located with a wide margin. So Light concrete block, Gypsum, Stone, Lime, and Concrete were identified as the most sustainable materials according to existing indicators respectively. The consequences of this study can help the project executors in order to promote the use of sustainable building materials in construction and also industries will be aware of the impact of the sustainability indicators on their products.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".