INTEGRATING BAR CODING AND RFID TO AUTOMATE DATA COLLECTION FROM CONSTRUCTION SITES
Bibliographic record
Abstract
Tracking and control construction projects depends primarily on the nature, accuracy, frequency and time required to collect onsite data of construction operations. Automated data collection methods can improve the speed and accuracy of data acquisition in a cost effective manner. The bar coding technology, for example, was introduced in 1973 (Shepard 2005) to automate the process of data collection. At that time it was compared with manual data collection. Subsequently, other technologies emerged to enhance the speed of data acquisition and circumvent some of the limitations of bar coding. RFID works in a manner similar to that of bar coding (Jaselskis 2003), whereas in RFID data can be stored in tags and retrieved with readers that can communicate with the tags using radio frequency waves instead of light waves as in bar coding. Implementing RFID into the construction industry is tied with the cost associated with that technology. The ideal approach is to replace bar coding with RFID. This paper presents a data collection methodology that utilizes both RFID and bar coding technology to track project cost and schedule information. RFID components are described and its applications into different industries are highlighted. A comparison is also given between RFID and bar coding to highlight the advantages and limitations of each. This paper also identifies construction data needed for tracking and control processes and examines the best technology to be used to collect this data. Criteria like the cost associated with each technology such as the price of transponders and scanners and the use of active and passive RFID tags are taken into account. The proposed methodology is part of an undergoing research that integrates different data acquisition technologies to automate the process of data collection from construction sites, compare it to planned ones, and subsequently generate progress reports.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".