BIM and genetic algorithm optimisation for sustainable building envelope design
Bibliographic record
Abstract
Decision-making (DM) at the early building design stages is essential to optimise sustainability performances. Nevertheless, the current methods of optimising building sustainability are complex as they involve multiple design variables and performance objectives. With the development of building information modelling (BIM), complicated buildings can be digitally constructed with precise geometry and accurate information for design optimisation in the early stages of project. Thus, this study explores the use of BIM and Genetic Algorithm (GA) to support DM and optimisation for sustainable building envelope design. To develop a BIM-GA optimisation method, Autodesk Revit template was created to extract data of building envelope from a Base Model (BM). Then, the data were employed to compute overall thermal transfer value (OTTV) and construction cost for BM evaluation and GA optimisation. A hypothetical building was modelled and then analysed using the proposed method as a test case. The BIM-GA optimisation method can address the difficulties of DM on building sustainability in the early design process.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".