An integrated RFID–UWB method for indoor localization of materials in construction
Bibliographic record
Abstract
A considerable body of literature exists on automated object localization and tracking of construction operations. While GPS-based solutions have been widely investigated in many studies for outdoor tracking of these operations, indoor tracking proved to be more challenging. This paper focuses on indoor material localization and investigates the use of two remote sensing technologies—ultra-wideband and radio frequency identification—and the integrated use of these technologies to leverage the benefits of each for a cost-effective and practical solution for location identification of materials on site. The developed method is based on an experimental study conducted in two phases. In the first phase, experiments are designed and performed to evaluate the accuracy of ultra-wideband for localization, as well as to determine the optimal output power for a hand-held radio frequency identification reader. The optimal power is identified by evaluating the range measurement accuracy and maximum reading range of the hand-held radio frequency identification reader. In the second phase, the integrated use of radio frequency identification device and ultra-wideband for object localization is studied, and an improved trilateration technique is developed. The results of the experiments show an absolute error of 0.52 m and 1.15 m for 2D and 3D localization, respectively. Accordingly, the integration of these two technologies eliminates the need for using a large number of radio frequency identification reference tags on site for indoor material localization. The method is expected to enhance automated material tracking on construction sites by improving the localization accuracy and providing a straightforward data acquisition protocol. The analysis of experimental data captured in a lab setting is also presented, demonstrating the advantages of the proposed method.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".