Automated Post-Production Quality Control for Prefabricated Pipe-Spools
Bibliographic record
Abstract
Prefabrications has been gaining popularity in the construction industry over the past decade as it provides a safer and more sustainable operation as well as higher quality and cheaper construction components, due to its controlled conditions during fabrication, in-house quality assurance systems, and lower amount of waste. Despite the advantages of prefabrication methods, the current quality management processes, particularly in piping fabrication, are labour-intensive, time-consuming, expensive and rather inaccurate. This paper investigates automated solutions for improving the quality management system associated with the prefabrication of piping assemblies in the industrial sector of the construction industry. The scope of this research is the quality management processes conducted at the post-production stage of the fabrication process. The findings of this paper indicated that 3D laser scanning and photogrammetry techniques can both be successfully used in quality assurance systems for post-fabrication of pipe-spools. It was concluded that these methods exceeded the accuracy requirements of the current systems, while substantially improving the efficiencies of the quality assurance processes for prefabrication operations.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".