Reality Data Capture for Reclaimed Construction Materials
Bibliographic record
Abstract
In recent years, opportunities are emerging to recover and reuse salvaged materials from building demolition and deep renovation projects to solve new design and procurement problems. However, the procurement process is often financially tasking due to the lack of well-detailed representations of reclaimed materials being readily available. Therefore, the exploitation of the advancement in 3D data capturing systems can aid the process and has already been applied widely for circular design strategies in building projects such as building adaptation, material refurbishment and regenerative design. Thus, this paper particularly explores the practicality of 3D scanning for reusable reclaimed elements at two secondary distribution sources in the Canadian environment. The paper outlines 3D material data capture processes at the two reclaimed material sources, using four different types of scanners to scan reclaimed elements like (1) structural steel, (2) windows & doors, (3) wood, (4) flooring, and (5) kitchenettes. Furthermore, the case study analyses the simplicity of the scanning process and the quality of the produced scans using commercialized scanners like; the faro laser, the freestyle, the dotproduct3D, and the iPad scanner. Based on the characteristics analyzed in this study, the two reclaim sources required different scanning systems due to the length and size of some of the building components and the layout of elements at each location. The findings in this study indicate the need to further assess scanning equipment for varying degrees of use across the industry to enhance equipment attunement, and advancements should support the movement towards a circular economy in the built environment.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".