Exploring the Interrelationship Between the Current and Future Sustainable Building Design Factors: UAE Perspective
Bibliographic record
Abstract
Sustainability plays an important role in protecting the surrounding environment from adverse impacts.It becomes a spotlight for researchers and engineers.The continuously developing social, economic, and environmental challenges need an evolution of the currentsustainable buildingdesignfactors with maintaining a direct relationship with the past andfuture.Based on reviewing the literature, the current factors are identified.However, there is a lacking in identifying future factors and exploring the interrelationship between current and future factors.This study identifiesthen validatesfuture factors by using the Delphi technique.Moreover, explores the interrelationship between factors by applying the multi-criteria decision making (MCDM), in particular the interpretive structural modeling (ISM) and cross-impact matrix multiplication applied to classification (MICMAC) methods.Finding the interrelationship will help future engineers in making decisions.A five-level model is generated which includes twelve factors, linking current and future factors.This model suggests that location and transportation factor is the key factor in the decision-making of designing sustainable buildings.Moreover, driving and dependence powers will guide the engineers in concentrating on the key factor.Accordingly, the factors are allocated into dependent and linkage clusters.Building space optimization factor has a high dependency on other factors as it is the only factor appears in the dependent cluster.While most of the factors appear in the linkage cluster which are unstable in the system and just transferring the effect.Future research work can consider other MCDM tools in validating theresults.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".