Developing a standard methodology for measuring and classifying construction field rework
Bibliographic record
Abstract
As the industrial construction sector in Alberta faces a period of megaprojects, cost and schedule overruns are becoming a major concern for both owners and contractors. One factor that often contributes significantly to these overruns is construction field rework. Despite the significance of rework, there are few industry standards available for defining, quantifying, and classifying field rework. This paper presents the results of a pilot study, conducted on one such megaproject, that attempts to develop a standard definition of construction field rework, a standard index for its quantification, and an approach for classifying the causes that lead to field rework so that they can be remedied. The data collection methodology developed is discussed, and the findings that arise from this methodology for the case study are presented. The main conclusion of this paper is that the proposed methodology is quite effective in its thorough analysis and treatment of the field rework issue, and it can be used as a first step towards an industry Best Practice for measuring and classifying construction field rework. It can now be used on subsequent projects over time to collect a sufficient dataset, from which the construction industry can develop industry standards and statistics on construction field rework.Key words: field rework, industrial construction, rework classification, rework index.
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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 | 0.035 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".