Modeling Construction Waste Generation towards Sustainability
Bibliographic record
Abstract
On-site waste management is considered as an important part of the sustainable construction process in any construction project. In fact, almost all Leadership in Energy and Environmental Design (LEED) projects require considerable effort in waste management as part of the requirement to obtain the LEED credits and rating. However, sustainable waste management is achievable only if the process is cost effective in addition to the underlying societal and environmental benefits. Planning on-site waste management process is essential to achieve economic benefits. However proper planning and execution is not possible without prediction of construction waste quantities linked to the project execution plan. The paper presents a novel method of grouping factors, thus reducing the number of variables for statistical analysis which requires for establishing relationships between quantity of waste and many factors such as labour, material and environmental related factors. . The research outlined in the paper considers the principles of "Activity Based Waste Generation" which enables the prediction of total waste from a project (i.e. cumulative waste from each of the activities). The presented methodology is part of the research on "developing a planning tool for construction waste management" which involves site monitoring, and data collections from several building construction projects in Calgary, Alberta.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".