Data Fusion Process Management for Automated Construction Progress Estimation
Bibliographic record
Abstract
This paper presents a process management framework for multisensory data fusion for the purpose of tracking the progress of construction activity. The developed framework facilitates the required type of data fusion at any given point in the construction progress, reliably and efficiently. Data are acquired from high-frequency automated technologies such as three-dimensional (3D) imaging and ultrawideband (UWB) positioning, in addition to foreman reports, schedule information, and other information sources. The results of validation through a detailed field implementation project show that the developed framework for fusing volumetric, positioning, and project control data can successfully address the challenges associated with fusing multisensory data by tracking activities rather than objects, a feature that offers superior capability, efficiency, and accuracy over the length of the project. Other contributions of this research include the development of fusion processes that are performed at higher levels of data fusion instead of traditional low-level fusion algorithms, thus supporting decision-making processes and a number of automated construction management applications, such as construction progress tracking, earned-value estimation, and schedule updating.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| 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".