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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it