Carbon Footprints Analysis for Tunnel Construction Processes in the Preplanning Phase Using Collaborative Simulation
Why this work is in the frame
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Bibliographic record
Abstract
Under the fast-developing carbon trading market, the construction industry needs to mitigate carbon emissions from construction processes. Among various construction processes, tunnel construction produces a significant amount of carbon emissions, since it utilizes various types of high energy-consuming equipment. In order to identify and mitigate such carbon emissions of a tunneling project, it is required to reliably estimate carbon footprints of a tunneling project in the pre-planning phase. This paper presents the methodology for estimating the carbon footprints generated during tunnel construction processes using the collaborative tunneling simulation. A case study using this methodology shows that carbon footprints from a utility tunnel construction are significant compared with those from a building construction. In addition, the assessment of carbon footprints of the case study identifies the opportunities to mitigate such impact by supporting decision-making on equipment and operation plans in the planning phase and providing a control target level of carbon footprints in the execution phase.
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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 | 0.001 |
| 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