Standardizing whole-building lifecycle assessment (wbLCA) benchmarking framework facilitated by adopting OpenBIM for Canadian building sector
Bibliographic record
Abstract
The industry is increasingly recognizing the importance of reducing greenhouse gas (GHG) emissions, leading to the widespread adoption of Whole-Building Lifecycle Assessment (wbLCA) as a crucial tool for evaluating environmental impacts.However, the effectiveness of wbLCA is limited by the lack of standardized benchmarking methods, resulting in significant variability in benchmark values.To address this issue, this paper proposes a framework for standardizing wbLCA benchmarking using a statistical model to enable comparability analysis, facilitated by both the adoption of Open Building Information Modeling (openBIM), and identification of optimal number of buildings to be studied.Utilization of the openBIM enables interoperability and standardization of data into the wbLCA, which enhance the accuracy and reliability of wbLCA.By leveraging openBIM, the framework aims to improve the transparency and comparability of wbLCA results, promoting informed decision-making for decision/policy makers.Furthermore, an optimal number of buildings, also known as sample size, is proposed to achieve optimal/adequate benchmarking values.The proposed framework is mainly based on modified graduated approach, and it enables identification of reference value and best practice value for residential and office buildings in the Canadian building domain, which enables reliable and comparable comparisons, and enable decision makers to set policies and standards accordingly.This paper outlines the key components of the framework, discusses the benefits and challenges of implementing Open BIM for wbLCA, and provides practical recommendations for policymakers and industry stakeholders to facilitate the adoption of standardization process through statistical model.This research supports sustainable building practices and the integration of BIM models into environmental assessments.
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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 | 0.021 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".