Analytical methods to reduce uncertainty in tunnel construction projects
Bibliographic record
Abstract
This paper presents a method to quantify uncertainty using simulation techniques and approximate geotechnical methods. Unknown soil conditions are major contributors to uncertainty in any underground construction project. Soil conditions are unknown because generally soil samples taken from vertical boreholes show only the soils present in the discrete borehole locations. The soil profiles between the boreholes therefore contribute to project uncertainty, and construction practitioners must make assumptions about these soil profiles for construction planning and scheduling purposes. Analytical and simulation methods are presented to accurately predict soil profiles between boreholes and reduce uncertainty in a "rough and ready" fashion. These methods use existing borehole data to create an analytical model for soil prediction, which is then incorporated with a process interaction simulation model of the construction project using special purpose simulation concepts and advanced geotechnical characterization techniques. The application of these methods to an Edmonton tunnel construction project is also detailed. Construction engineers or managers can use these simulation methods to strengthen the geological data obtained for the construction project.Key words: borehole data, construction, risk, soil families, soil profiles, soil transitions, special purpose simulation, tunnelling, uncertainty.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".