Activity-Based Data Fusion for Automated Progress Tracking of Construction Projects
Bibliographic record
Abstract
In recent years, many researchers have investigated automated progress tracking for construction projects. These efforts range from 2D photo feature extraction to 3D laser scanners and Radio Frequency Identification (RFID) tags. A multi-sensor data fusion model that would utilize multiple sources of information would provide a better alternative than a single-source model for tracking project progress. However, the existing fusion models are based on data fusion at the sensor and object levels, and therefore, are incapable of capturing critical information regarding non-structural trades and activities on a construction site, such as welding, inspection and installation activities. This paper presents an activity-based data fusion model, which incorporates an Ultra Wide Band (UWB) positioning system to track activities in a construction project. A field experimentation study on an industrial-type building construction project was conducted to validate the model presented in this research. The scope of the experimental program was limited to ductwork, HVAC, and piping activities on the project, but the model, experiments, and results are scalable to a complete construction project. A comparison of concrete, steel, and piping projects showed that for piping projects, where the asbuilt environment may be substantially different than as-designed models, the activity-based progress estimation model of this paper can be fused with existing object-based models to provide a more accurate and reliable progress estimate.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".