A Building Information Modeling Approach for Adaptive Reuse Building Projects
Bibliographic record
Abstract
Adaptive reuse of buildings is considered a visionary practice and a superior alternative for new construction, in terms of sustainability. It is considered key for transitioning from a resource-based construction economy towards a circular one. Current approaches to support project design, planning, and execution with building information modelling (BIM) are insufficient to support adaptive reuse projects. BIM is insufficient when we think of adaptive reuse as a flow of building materials and components through a circular value chain, and when we conceive of existing assets as the future source of construction materials. In this paper, we show with examples that current BIM models do not support important project activities of adaptive reuse projects. Then, we identify needs and requirements for information models that support these activities. The requirements focus on how to effectively represent parts, materials, and systems, as well as, interfaces between them. We will also suggest additional properties that need to be defined in BIM models for adaptive reuse projects. The main contribution of this study is the development of a framework for an integrative BIM approach for improving adaptive reuse projects outcomes inside of a circular economy (CE) context.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".