Retrofitting of building components using Building Information Modelling for sustainability
Bibliographic record
Abstract
The use of energy-efficient and environmentally friendly materials and techniques is essential to mitigate climate change, especially for existing building structures, which contribute to carbon emission and increased energy demand. Retrofitting existing buildings is the most effective approach to meet sustainability goals. Building Information Modelling technology is used to implement green roofs in four existing structures. Carbon emission and following solar analyses for energy estimations were done with the help of standard tools: direct solar gains, monthly solar exposures, monthly heat gains, and comfort levels. The retrofitted building models were created with green roofs and material changes in walls and windows. Thermal transmittances (W/m2K) of green roofs, walls, and windows were compared between existing and retrofitted models. The core objective is to improve building energy efficiency and reduce carbon dioxide emissions by using a good mix of different passive design measures, provide energy-efficient solutions for the existing structures, and develop a building scale framework that can be followed to make existing structures energy efficient. Overall, retrofitting lowered the thermal transmittance of the roof, walls, and windows in all buildings. The retrofitted model enhanced the sustainability of all chosen buildings as indicated by the solar analyses and carbon dioxide emissions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".