A Data-Driven Framework for Automated Generation of PC Component Trailer Arrival Times: Integrating Work Interruptions Simulation and Duration Prediction
Bibliographic record
Abstract
Currently, site managers at Precast concrete (PC) construction sites are determining arrival times using simplified methods without considering duration variability and work interruptions, resulting in frequent site congestion and work delays. To address these issues, this research proposes a framework for a data-driven for automated generation of PC component trailer arrival times. By presenting multiple arrival time options according to various confidence intervals, the framework provides site managers with a flexible decision support tool that can be tailored to specific project needs. This framework will contribute to the improvement of efficiency and economic feasibility of PC construction by systematically managing the uncertainties of on-site operations. Through this framework, the limitations of existing methods that rely on experience and intuition can be overcome, and construction companies are expected to implement decision support tools optimized for their specific site characteristics using independently collected data.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".