Decision support system (DSS) for selecting sustainable insulation material using Pareto search and novel fuzzy-modified technique for order of preference by similarity to ideal solution (TOPSIS) approach
Bibliographic record
Abstract
Efforts to improve the sustainability of the building construction sector have tended to focus on reducing operational energy cost, whereas sustainability considerations in the material selection at the design and construction phase have received less attention due to construction budget constraints. The present study proposes a decision support system (DSS) to assist decision-makers in accounting for sustainability in their construction material selections. We demonstrate how the developed DSS can be used to identify the most sustainable insulation materials and thicknesses among commercially available alternatives. The DSS ranks available alternatives by incorporating individual project information, material information, and the decision maker’s preferences. Technique for order of preference by similarity to ideal solution (TOPSIS) and Pareto search technique are combined in the methodology. By limiting the alternatives to the ‘Pareto front’ of life cycle assessment (LCA) and life cycle cost in a multi-objective optimization setting, we seek to reduce subjectivity in the multi-criterion decision-making process. Moreover, product-specific environmental product declaration is used to calculate the embodied energy for the LCA. The framework recommends commercially available materials and thicknesses accordingly. The proposed method is programmed in Python to establish a user interface for data input and output of 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".