Combining Large-Scale 3D Metrology and Mixed Reality for Assembly Quality Control in Modular Construction
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
The quality control (QC) of assembled modules is an essential process when constructing modular buildings such as hotels and hospitals. Defects that go undetected during module assembly may result in lost productivity in the form of unnecessary transportation, rework or project delays. QC has traditionally been performed using specialized tools and carried out a posteriori in an inspection station dedicated solely to this task. Nowadays, large-scale 3D metrology technology provides a more efficient alternative since it enables accurate measurements to be taken in situ. Additionally, mixed reality (MR) supports the immersive projection of information and guidance instructions. This paper introduces a proof of concept of a framework that combines industrial photogrammetry with the HoloLens 2 MR headset to assist with assembly and QC during the off-site construction phase of modular construction. Many tests were conducted in a laboratory and a factory setting to evaluate the system’s user-friendliness and possible challenges associated with its future implementation. The experiments conducted confirmed that combining 3D metrology with MR offers an interesting solution for integrating QC into the assembly process. However, further work is needed to enhance the measurement workflow and optimize the measurement system’s accuracy
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) | 0.001 | 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 | 0.001 | 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