Near-Zero Rebar Cutting Waste Management by Adjusting Lap Splice Position
Bibliographic record
Abstract
In general, rebar cutting waste is estimated to be 3-5% in the construction planning stage.However, technology to reduce RCW was not developed at the construction field, so more than 5% is generated in the actual construction.To solve this problem, many studies was conducted to minimize RCW.Most studies proposed methods to minimize RCW by using stock lengths or market lengths, referred to as standard.In other words, the rebar shown in the structural drawings is combined using the rebar mill or the stock length held to minimize cutting waste.RCW can be reduced if rebars ordered in special lengths are used in rebar combinations.Reducing rebar cutting wastes to near-zero rebar are necessary in terms of cost reduction and sustainable construction.Therefore, the purpose of this study is a basic study of near-zero rebar cutting waste management by adjusting lap splice position.As a result, the optimal amount of rebars in the case site was 17.74 tons, with the rebar cutting waste ratio reduced to less than 1%.In addition, the amount of rebar was reduced by 0.53 tons, which is 2.93% of the actual quantity.About 284 USD was saved, and 1,872 kg-CO2 was reduced.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".