Building stock as a future supply of second-use material – A review of urban mining methods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 it