Building stock as a future supply of second-use material – A review of urban mining methods
Bibliographic record
Abstract
The building sector is a major player in the world’s contribution to climate change, partly due to its dependence on large quantities of materials. The circular economy model of material flow has been gaining attention in the past decade as it seeks to promote the use of construction, renovation, and demolition (CRD) waste as inputs for new buildings or other applications, which would result in the diversion of materials from landfills. Developing a system capable of handling such waste requires a comprehensive knowledge of the composition of the building stock materials. This information, however, is rarely available. Thus, this research is proposing a conceptual model to aid city planners when considering the existing built environment as a resource for new construction. The methodology followed by this review includes a thorough analysis of 82 articles on quantity takeoff methods in the Urban Mining (UM) and CRD Waste Management (WM) fields. These articles were analyzed by considering a framework of four layers, i.e., (i) the approach, (ii) the analysis method, (iii) the granularity, and (iv) the performance analysis. The comprehensive analysis of the literature has highlighted the fact that the existing quantity takeoff methods need to consider more in-depth attributes and that the works performed by using machine learning methods are very important in the path toward the direction of improving these methods. With this conceptual model, waste management planners can select the appropriate methodology based on the available input data, and the type of output that they are looking.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".