Selection of rework measurement methodology utilizing AHP method
Bibliographic record
Abstract
Abstract Civil construction has high rework rates, which entails additional costs and delays in project deadlines. In this way, several authors and entities have studied the problem and sought solutions to try to quantify and minimize the consequences of reworking. Several rework measurement methodologies have been developed, including: Construction Industry Institute Reduction Rework Program, Best Productivity Practices Implementation Index of the Construction Industry Institute, Methodology of the Construction Owners Association of Alberta, and Measuring and Classifying Construction Field Rework . In this context, this article aims to propose a procedure, based on the multicriteria analysis method Analytic Hierarchy Process – AHP, to assist in the process of selecting the most appropriate rework measurement methodology to be adopted in subsectors of the civil construction industry. The proposed procedure was applied to the industrial assembly segment, and the Rework Reduction Program methodology presented the best results regarding the rework measurement according to five criteria selected for analysis (coverage, deployment, costs, data entry and system operation), being the most indicated for use.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".