Developing a Reclamation Framework to Promote Circularity in Demolition Projects
Bibliographic record
Abstract
The construction, demolition, and renovation industries are among the largest contributors to global carbon emissions and waste. With decreased landfill capacities, increased waste diversion targets, resource shortages, and the recognition that material waste is critical to climate change, diverting demolition waste is now a significant priority in waste management. Deconstructing a structure and reusing its building components can significantly reduce the environmental burdens imposed. However, to optimize the reuse of building materials and components for their environmental, societal, and economical benefits, the reclamation procedure must be undertaken in a more rational and robust manner. There are currently gaps in frameworks and tools that involve the assessment of reusable building components in demolition projects. This paper develops a reclamation framework to assess the viability of recovering and reusing building components. The framework first describes a process for conducting a technical audit and uses an assessment tool to suggest a level of deconstruction based on the physical parameters of the building circumstances. The framework complements this initial outcome by then assessing additional comprehensive parameters, such as the cost, the heritage value, and the available timeframe to arrive at a suggested outcome of actions, which can range from complete demolition and basic material recovery to deliberately removing salvageable items. The framework is then applied to an older, detached office building as a conceptual case study for demonstration. The recommended level of deconstruction appears appropriate based on the visual assessment of the structure. The result of this paper promotes the circular economy and supports the United Nations Sustainable Development Goals (UN SDGs) by presenting a notably more insightful and guided approach to capturing deconstruction waste.
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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 | 0.031 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.004 | 0.003 |
| 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".