Automated Deviation Analysis for As-Built Status Assessment of Steel Assemblies and Pipe Spools
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
Steel assemblies and pipe spools play an essential role in the industrial construction sector. Fabrication of steel assemblies has been a challenging task due to the limited fabrication precision of the tools used in the process and inadequate inspection during fabrication. Moreover, unfavorable deformations may occur during the transportation phase which makes the erection and installation phase more complicated. These deviations require further considerations for realignment and repair that are associated with rework on construction sites. Hence, a systematic and automatic framework is required to continuously monitor the fabrication and installation processes of steel assemblies. Current approaches lack a sufficient level of control and are prone to error. This paper presents an automated framework to detect defective parts in steel assemblies and pipe spools in particular. A laser-based point cloud, which represents the as-built status, is compared to the original state from the CAD drawings that exist in the Building Information Model (BIM). Therefore, the defective parts are detected in a timely manner. The comparison is distance based and the procedure is fully automated. The experiments conducted to validate the proposed approach show that the model has high precision and a high rate of recall and has the potential to be employed for automated damage detection in order to improve productivity on construction sites.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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