Optimizing hybrid insulation systems for diverse climates: A comparative analysis of composite material combinations in residential buildings
Bibliographic record
Abstract
With increasing concerns over global warming and climate change, achieving environmental sustainability in residential construction has become a priority. Traditional insulation materials often struggle to maintain optimal thermal and energy efficiency across varying climatic conditions, highlighting the need for innovative hybrid systems. This study systematically evaluates hybrid insulation systems through simulation-based analysis and a multi-objective mathematical model to optimize energy performance in residential buildings. Simulations were conducted to analyze different material configurations in two distinct Canadian climates: the mild conditions of Vancouver and the cold environment of Winnipeg. The assessment considered thermal resistance, energy consumption, and operational emissions. A multi-objective binary integer programming model was developed to prioritize material combinations based on five key criteria: accessibility to materials, total energy consumption, operational cost, operational environmental impacts, and societal aesthetics. The model incorporated priority weights to align with diverse stakeholder preferences, enabling decision-makers to tailor the optimization process based on specific goals. The results demonstrate that Combination 4, comprising limestone, oriented strand board (OSB), and clay tiles, consistently outperformed other configurations in both climates. In Vancouver, this combination reduced energy consumption by 47.7% compared to a conventional 6-inch concrete wall, while in Winnipeg, it achieved a 49.8% reduction. Furthermore, Combination 4 exhibited the lowest operational emissions and costs, making it the most cost-effective and sustainable choice. These findings provide valuable insights for architects, policymakers, and construction professionals seeking resilient, energy-efficient, and environmentally sustainable insulation solutions adaptable to diverse climatic conditions.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".