Enabling Automatic LCA at Any Stage of the Building Based on Its BIM Model
Bibliographic record
Abstract
The Life Cycle Assessment (LCA) of a whole building is a well-known process used to assess its environmental impact. The construction domain does not use this process at this time because it requires too much information and collecting it is very labor intensive. This paper identifies the information needed to perform an LCA at any level of development of a Building Information Modelling (BIM) model and proposes some solutions to fill the information gap of an early stage BIM model. After the required information is identified, the interoperability strategy is analyzed to propose a framework introducing a way to organize the LCA of a whole building, as well as a new file format to share information between BIM and LCA software. The proposed framework enables an LCA to be performed, without manual input, at every iteration of the BIM model. This framework was previously presented at the Creative Construction Conference 2019 and this paper is an extended version of that paper.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".