The Autonomous Real-Time System for Ubiquitous Construction Resource Tracking
Bibliographic record
Abstract
The Autonomous Real-Time System for Ubiquitous Construction Resource Tracking M. Soleimanifar, D. Beard, P. Sissons, M. Lu, M. Carnduff Pages 1428-1436 (2013 Proceedings of the 30th ISARC, Montréal, Canada, ISBN 978-1-62993-294-1, ISSN 2413-5844) Abstract: With more and more industrial construction projects implementing RFID and other sensor-based technologies on fabrication and project sites, new innovative processes are needed to help automatically read and re-position RFID tags while reducing the cost to deploy RFID infrastructure over large areas. RFID tags are being used on construction sites to help identify the location of materials, equipment and personnel to aid in finding project critical materials and equipment required to construct the industrial facility on time and on budget. This research provides a cost-effective process for reading tens of thousands of RFID tags over large project sites from outdoor laydown yards, to warehouses to the workface where the materials and tagged equipment are installed. Based on the collected RFID and sensor data, localization mechanisms determine the most recent coordinates of the tagged components. The paper will cover analysis of RFID collected data and key lessons learned from a commercial deployment of the SiteSense® system with one Alberta-based industrial contractor, JV Driver Fabricators, at an 80-Acre module, pipe spool and structural steel fabrication site. Keywords: Construction, asset tracking, laydown yard, RFID, GPS, fabrication, pipe spools, modules, materials management, warehouse DOI: https://doi.org/10.22260/ISARC2013/0162 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".