BIM and Tunnelling – a Norwegian application: the Sotra Link Project
Bibliographic record
Abstract
The adoption of the BIM approach (Building Information Modeling) is an established practice for infrastructure design in Nordic countries and is gradually becoming a mandatory requirement for big projects in other developed countries such as Italy.The current paper deals with the application of BIM to the Sotra Link Project (SLP), a complex project of several underground and surface structures between Sotra island and the city of Bergen (Norway), including a 900 m-long suspension bridge and 12.5 km of tunnels.Adopted from the Early Design to the Detail Design of Sotra Link Project, BIM allowed a fruitful information exchange between design teams and client, the creation of a comprehensive design database, and so ensuring design consistency across disciplines, cost optimization and time saving.Within the frame of tunneling, the implementation of innovative discipline-specific workflows including project databases, BIM modeling and computational design software enabled the creation of a comprehensive geotechnical/structural model of all the tunnels involved in the SLP, which constituted a unified data source for deliverables, bill of quantities, and validation of the final design.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".