Developing a standard methodology for measuring and classifying construction field rework
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 it