Building material selection framework for tropical climatic conditions: Eco-design-based approach
Bibliographic record
Abstract
All over the world, sustainability has been given immense attention, thus novel state-of-the-art materials and building systems are emerging as alternatives. With those different alternatives, comparison and the selection of a better-performing material or a building system using diverse perspectives becomes important. Literature shows its complexity in adopting social and cultural dimensions in decision-making that adversely impacted on most desirable material selections in tropical underdeveloped countries as well as indigenous communities. This study proposed an eco-design-based material selection approach that considers the individual and holistic perspective of diverse eco-design indicators such as economic, ecological, social, and cultural. Several sub-themes are identified under each theme and are verified through expert surveys. The identified sub-themes are verified and compared Pairwise through expert surveys and using the Analytical Hierarchy Process leads to proposing weights for each theme and sub-theme and developing an eco-design-based material selection framework. Accordingly, saving energy (10.7%), reducing the overexploitation of natural resources (9.1%), reducing energy emissions (7.9%), and reducing construction (7.3%), as well as operational cost (7.6%), are the parameters that create a greater impact on the selection of sustainable material with the aid of eco-design. Overall, the ecological theme creates the highest impact of around 46% and the cultural theme contributes to a lower percentage of around 5% while the economic and social themes impact considerably 30% and 19% respectively. Furthermore, the developed framework is tested in a case study by comparing an emerging walling material, Cement Stabilized Earth Blocks with conventional materials such as Burnt Clay Bricks and Cement Sand Blocks . Technique for Order Preference by Similarities to Ideal Solutionwas carried out to compare and rank the respective walling materials under different scenarios. Accordingly, Cement Stabilized Earth Blocks is concluded to be the best alternative when analyzed in the eco-design concept. The proposed framework could be highly beneficial for industry practitioners in tropical underdeveloped countries and indigenous communities in choosing community-preferred, affordable, and environmentally-friendly construction materials.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".