Using digital photogrammetry for pipe-works progress tracking<sup>1</sup>This paper is one of a selection of papers in this Special Issue on Construction Engineering and Management.
Bibliographic record
Abstract
Pipe-works are among the most complicated items to be tracked in the course of monitoring construction project progress. Traditionally, the tracking of pipe-works progress is conducted either manually or using laser scanning technology. While laser scanning is a 3D imaging technique, and commercial software exists to construct 3D CAD models of piping based on such images, it suffers from portability, purchase cost, and other disadvantages. This paper describes digital photogrammetry technology as an alternative for pipe-works reconstruction and as a cost effective tracking tool. For validation, data was collected using a handheld digital camera to acquire images inside a new building under construction. Progress of the pipe-work networks of different types and sizes in the new building was monitored during the construction phases. In addition to the known accuracy and robustness of photogrammetry, it was found that the use of digital photogrammetry provided a practical and low-cost approach.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".