Framework for Lifecycle Status Tracking and Visualization of Constructed Facility Components
Bibliographic record
Abstract
ABSTRACT: Four-dimensional (4D) models link 3D construction models with schedule data. The visual representation of the schedule is capable of facilitating decision making during the planning and construction phases as well as the maintenance phase. However, the process of conventional data collection methods for progress monitoring is labour intensive, time consuming and error-prone; therefore, updating the virtual model based on status information is not practical. In this research, we propose a framework for lifecycle status tracking and visualization of constructed facility components using Radio Frequency Identification (RFID) technology. The proposed approach facilitates and enhances the process of data acquisition not only during the construction phase, but also during the subsequent maintenance phase. The components of the facility are tagged after being manufactured with RFID tags that remain on the components throughout their lifecycle. Each component is scanned in several phases and data are collected. The real-time interaction between the facility and its virtual model results in automatic creation of an accurate 4D model which can be used for progress monitoring and visual comparison of the planned schedule against the actual progress. The feasibility and challenges of the proposed framework are discussed and demonstrated using a prototype system and a real-world case study. KEYWORDS: RFID, Progress Management, 4D Modeling, Lifecycle status tracking, Visualization. 1.
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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".