Usage of Interface Management System in Adaptive Reuse of Buildings
Bibliographic record
Abstract
Adaptive reuse of buildings is considered a superior alternative for new construction in terms of sustainability and a disruptive practice in the current capital project delivery model for the renewal of today’s built environment. In comparison to green-field construction projects, adaptive reuse projects require distinct stages, definition of interfaces, decision gates, and planning methods in order to secure the success of the building project. Unfortunately, little research has been done regarding establishing feasible systems for the planning, assessment, and management of adaptive reuse projects, leading to underperforming building projects outcomes. Interface management (IM) can improve renovation projects outcomes by defining appropriate ways to identify, record, monitor, and track project interfaces. IM has the potential of bringing cost and time benefits during adaptive reuse projects execution. The aim of this study is to develop a reference framework for implementing IM for adaptive reuse projects. First, the inefficiencies of redevelopment projects are explained inside of a circular economy (CE) context. Second, an ontology of IM for adaptive reuse projects is defined based on the current barriers to adaptive reuse and the most common interface problems in construction projects. Third, the defined ontology is expanded through a case study by showing examples of adaptive reuse barriers on a case project, and how IM could have been part of the solution for these problems. Finally, this study concludes with the suggestions on interface management systems (IMS) implementation for future adaptive reuse projects.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".