Mixed Reality-based Digital Twinning of Building Circularity: A Co-Design Approach for Sustainable Buildings
Bibliographic record
Abstract
Sustainability in construction practices is becoming the need of the day, and the construction sector is getting adoptive to the integration of digital tools to explore the opportunities for enhancing sustainability and circular economy applications, offering significant benefits to both industry and society. To achieve this goal, this article discusses a sustainability framework combining Digital Twin (DT), Mixed Reality (MR), and Life-Cycle Assessment (LCA) to align with circular economy principles in building construction.A case study of a single-family house in Kelowna, BC, Canada is conducted to demonstrate the potential of this integration for comprehensive LCA of buildings.The LCA analysis of the building is performed using OneClick LCA-an LCA platform.The results of LCA account for the embodied carbon, improved material circularity, life cycle cost efficiency, etc.A DT Dashboard (DTD) of the building's circularity model is also developed, which monitors and optimizes the whole life cycle of the building.The DTD is then deployed to MR hardware -Microsoft HoloLens for enabling onsite circularity analysis of the building.This not only allows for immersive visualization of LCA data but also enhances stakeholders' collaboration and decisionmaking.This way the study showcases how immersive DT can potentially be a game-changer for sustainable construction and provides an industrially replicable model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".