Simulation case study: modelling distinct breakdown events for a tunnel boring machine excavation
Why this work is in the frame
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Bibliographic record
Abstract
Tunnel Boring Machine (TBM) tunneling projects are frequently hit with delays which can cause adverse effects, extending schedules and incurring additional costs. This paper outlines a case study to show how simulation can be effectively used to analyze productivity performance of a project with emphasis on delays from equipment breakdowns and unexpected conditions. Data collected from this project under a Method Productivity Delay Modelling study, completed by a consulting firm, was collected and prepared to model delays on a combined discrete event continuous tunneling simulation model. Calibration was done to the theoretical tunneling model to ensure the results would be reflective of the actual construction project and to measure the effectiveness of the delay modelling. Sensitivity analysis was conducted to distinguish the most unfavourable delays to a tunneling project, allowing further analysis into the results of the mitigation of these delays on project duration and hypothetical costs.
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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 | 0.000 | 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 it