Assessing the Applicability of Green Building Information Modelling for Existing Green Buildings
Bibliographic record
Abstract
Green Building Information Modelling is a novel approach which address the momentous need of sustainability. It offers more standardized way of creating buildings which in turn optimize the building performances while attending to the sustainable requirements. Comprising numerous techniques, Green BIM ensures more accurate and comprehensive building performances to optimize the energy efficiency of buildings. Thus, there is a considerable advantage to create more energy efficient buildings using Green BIM. However, the use of Green BIM for existing green buildings is very less while new green buildings are managed using Green BIM. Hence, this research focused to assess the applicability of integrating Green Building Information Modelling for existing green buildings through a practical implementation and identifying the potential challenges. Conducting a comprehensive literature survey first, Green BIM techniques and data requirements of Green BIM were reviewed. A single case study was conducted next, to practically apply the Green BIM technology. The data requirements for Green BIM were identified prior to the selection of case for the study. A questionnaire survey was conducted to analyze the data availability including the data identified from literature. Analyzing the survey findings, an existing green building was selected and finally, the steps to be followed for the use of Green BIM were established. The challenges of the implementation were also presented in this study. The findings proved that; Green BIM is a potential technology for the existing green buildings.
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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 | 0.015 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".