Carbon Footprints Analysis for Tunnel Construction Processes in the Preplanning Phase Using Collaborative Simulation
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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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".