Assessment of recycled ceramic-based inorganic insulation for improving energy efficiency and flame retardancy of buildings
Bibliographic record
Abstract
In addition to the mitigation of carbon emissions through the reduction of building energy consumption, the prevention of fire spread in buildings is important an important task globally. Therefore, a growing interest towards building materials that can simultaneously contribute to energy savings and provide good flame-retardant performance in buildings exist. The flame-retardant performances of buildings can be improved through the use of inorganic building materials during construction. Meanwhile, among the different types of construction waste, more than 70% of ceramics can be recycled, which would reduce carbon emissions in the production process. Ceramics are inorganic and non-flammable, and can thus secure the flame-retardant performance of buildings. In this study, recycled ceramic-based inorganic insulation to secure the flame-retardant performance of a building are analyzed for their energy saving values. A case study building was modeled and the flame-retardant performance and building energy consumption were analyzed. Setting the thermal transmittance of the external wall according to the energy conservation design standards in South Korea, the tradeoff between model calculates annual energy consumption fire protection and minimization of material environmental impacts are discussed. As a result of simulation, when a wall constructed according to the energy conservation design standards of buildings, the building energy was saved by 18.6% and fire resistance performance was secured.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".