An Automated BIM Model to Conceptually Design, Analyze, Simulate, and Assess Sustainable Building Projects
Bibliographic record
Abstract
Quantifying the environmental impacts and simulating the energy consumption of building’s components at the conceptual design stage are very helpful for designers needing to make decisions related to the selection of the best design alternative that would lead to a more energy efficient building. Building Information Modeling (BIM) offers designers the ability to assess different design alternatives at the conceptual stage of the project so that energy and life cycle assessment (LCA) strategies and systems are attained. This paper proposes an automated model that links BIM, LCA, energy analysis, and lighting simulation tools with green building certification systems. The implementation is within developing plug-ins on BIM tool capable of measuring the environmental impacts (EI) and embodied energy of building components. Using this method, designers will be provided with a new way to visualize and to identify the potential gain or loss of energy for the building as a whole and for each of its associated components. Furthermore, designers will be able to detect and evaluate the sustainability of the proposed buildings based on Leadership in Energy and Environmental Design (LEED) rating system. An actual building project will be used to illustrate the workability of the proposed methodology.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.006 | 0.002 |
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".