Material selection of sustainable composites by the valorisation of plastics and agro wastes: An integrated q-rung orthopair fuzzy-based multiple criteria decision making model
Bibliographic record
Abstract
• Recycling of plastics and agro-industrial wastes for green building materials. • A systematic fuzzy based SWARA- COPRAS approach for material selection. • Effectively deal with vagueness and uncertainty in decision makers. • Comparative and sensitivity analyses of the mathematical model. The concern for plastic wastes and high usages for building construction materials has promoted the nation towards eco-friendly composite materials for structural components. Anappropriate choice of material improves the functionality and life cycle of a product . The research seeks to promote sustainable practices in building construction for the attainment of the circular economy. This investigation anticipated a fuzzy number-based integrated Stepwise Weight Assessment Ratio Analysis (SWARA)-Complex Proportional Assessment (COPRAS) mathematical model for material selection of eco-friendly composites. In the present work, eight (08) different composites are developed by recycling of waste plastics namely low-density polyethylene, high-density polyethylene, polypropylene, and polyethylene terephthalate with the reinforcement of natural fibres of coconut and jute. The alternatives are ranked by considering seven (07) criteria for structural application such as floor tiles, pavements, panels, etc. The compressive is identified as the most significant while hardness is least preferable criteria for composite having structural applications. The proposed model has identified the alternatives A6 and A1 as the most and least preferable alternatives, respectively. The research has recommended the incorporation of 20 wt.% of jute fibre with 80 wt.% of polypropylene in composites for structural applications. The comparative analysis of rankings against the other well-known techniques has verified the trustworthiness of the model. The high ranges of 0.76–-0.928 for Spearman’s rank correlations coefficient ( r s ) has verified the robustness of the ranking results. The sensitivity analyses have shown the influence of criteria weight on rankings. The suggested mathematical approach can efficiently rank the composites and address the challenges associated in the material selection of polymeric composites in unpredictable environments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".