Data Delivery for Standardizing Sustainable Whole-Building Lifecycle Assessment Using the Proposed OpenBIM Framework
Bibliographic record
Abstract
Whole-Building Lifecycle Assessment (wbLCA) is an essential tool for evaluating the environmental impacts of buildings, including the benefits of material reuse. wbLCA can help the Architecture, Engineering, Construction, and Operation (AECO) industry reduce greenhouse gas (GHG) emissions. However, the effectiveness of wbLCA depends on the availability, quality, and exchange of data, leading to significant inconsistencies in LCA assessments and analyses. To address the issue of data consistency and reliability in wbLCA, this paper proposes a framework that uses the openBIM approach to standardize the required data. By using BIM to procure data for LCA, the framework ensures data accuracy and consistency, facilitates data transfer into wbLCA, and improves assessment reliability by minimizing redundancy and modeling errors. OpenBIM standards and tools are adopted to validate Industry Foundation Classes (IFC) submittals, and the models are checked for both geometric and non-geometric data needed for LCA. This comprehensive approach ensures that the model includes all necessary information for LCA analysis, assessment, comparison, and benchmarking. This openBIM-integrated approach is considered a key contribution to aid standardization of wbLCA practice and to add records to the bill-of-work database, which could host records for hundreds or even thousands of buildings. This sophisticated, flexible, and dynamic approach enables self-updating peer model identification, in contrast to static, generic ‘archetype’ baseline buildings. As the database grows, regional and building-type specificity will increase. The study provides practical recommendations for industry stakeholders and authorities.
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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 | 0.024 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".