A Framework to Enhance Utilization of Automated Progress Measurements in Construction Projects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Deviation between project baseline and actual performance is a common challenge in the construction industry. Traditional monitoring and tracking of construction project status is labour-intensive, error-prone, time-consuming, and costly. Despite research and technological developments, the optimal level of efficiency in automated construction project measurement has not been met. Not achieving the desired automation efficiency is caused by a significant gap between using automated progress measurements and traditional project measurement systems. This requires a structured framework for integrating automated data acquisition and progress measurement through baseline plans. This paper proposes a systematic, generic framework to integrate baseline development with 5D Building Information Modeling (BIM) and automated project monitoring and measurement. The integration process involves using work packages and S-curve to automatically measure progress values. Also, interviews with five industry experts were conducted to assure the applicability and feasibility of the proposed framework. Developing this framework will help the construction industry to maximize the benefits of implementing automated project measurements and minimize the problems of traditional measurement methods.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it