THE USE OF EXPLORATORY TUNNELS AS A TOOL FOR SCHEDULING AND COST ESTIMATION
Why this work is in the frame
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Bibliographic record
Abstract
Exploratory tunnels are commonly used for examining the geotechnical and structural aspects of proposed tunnel alignments. This paper explores the utilisation of exploratory tunnels as a project management tool for estimating the cost and duration of construction for the entire project. Data were collected from the Kaponig 2,75 kilometers exploratory tunnel, a part of a double‐track high‐speed railway development in Austria. This knowledge and experience was used to evaluate the risks associated with design details for the final tunnel enlargement (alignment and grade, support requirements and excavation methods). A deterministic model based on Monte Carlo simulation was developed capable of predicting potential outcomes of the total project in terms of cost, duration and their associated probabilities.
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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