RFID deployment protocols for indoor construction
Bibliographic record
Abstract
Purpose Location awareness is essential to decisions pertinent to tracking and progress reporting, as well as to safety in construction projects. However, these applications have been mostly limited to the outdoor environment, where satellites for positioning information are in view. Recent studies on indoor location sensing systems are now overcoming this limitation and offering significant potential on construction practices, and radio frequency identification (RFID) is the most widely utilised technology for such application. The purpose of this paper is to address a wide range of protocols that are vital for RFID deployment for indoor construction. The paper identifies deployment settings to provide data acquisition with higher accuracy for indoor location sensing in construction. Design/methodology/approach A computational platform was designed to assess and evaluate the most suitable condition related to deployment of reference tags in construction. In this platform, a number of protocols and parameters are presented and their performance is evaluated. The evaluation scenarios were performed on a construction facility in Montreal, as well as in a controlled lab environment. The computational platform used for the study comprises the use of passive reference RFID tags and K Nearest Neighbour algorithm (K‐NN) for course‐grained detection of target's location and its classification into pre‐defined zone areas. Findings The studies resulted in a number of observations, findings, and lessons learned for RFID deployment in construction. The results indicate that: the speed of the reader is in direct relationship with the detection error rate; zone configuration effectiveness is in direct relationship with the deployed RFID read‐range; error rate on the controlled environment is significantly lower than rates in construction site; and stationary reader performs better than moving reader. Originality/value The paper's findings are expected to be of considerable value to researchers and practitioners involved in the utilisation of RFID technology in construction. The paper provides a set of helpful protocols for the deployment of passive RFIDs for automated onsite management of construction operations.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".