Prefabrication as a mean of minimizing construction waste on site
Bibliographic record
Abstract
Construction waste has become a major source of solid waste in Hong Kong. Thousands of tons of solid waste is produced every year from construction and demolition activities. Increasing generation of this waste has caused significant impacts on the environment and aroused public concerns. Therefore, minimization of construction waste has become a pressing issue. This paper aims to (i) reveal the status of construction waste, (ii) investigate the effectiveness of prefabrication in terms of waste reduction in replacing the traditional on-site production, (iii) examine the factors that help minimizing construction waste by the adopting prefabrication and (iv) explore the areas of waste reduction after adoption of prefabrication in comparison to traditional on-site production. The findings of a structured survey show that waste from ‘poor workmanship’ can be greatly reduced by adopting prefabrication in construction. Furthermore, after the adoption of prefabrication, waste generation can be greatly reduced in various on-site production activities, including plastering, timber formwork, concreting and reinforcement, with 100% waste reduction seen in plastering. Case studies are also used to demonstrate the effectiveness in the use of prefabrication to minimize construction waste in Hong Kong. It can be concluded that using prefabrication of building components is one of the most effective technologies of waste minimization.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".