Probabilistic Rock Mass Classification from Prior Construction for the Future Extension of a Tunnel
Bibliographic record
Abstract
This paper presents a procedure to re-analyze the original design of the Eisenhower-Johnson Memorial Tunnel (EJMT), which opened in 1973 and 1979 when rock mass classification systems were being introduced.The re-analysis used a new probabilistic approach to rock mass classification and stochastic representation of ground and support parameters.The results will be used to select the alignment of future extensions of the EJMT.Rock mass classification systems using Q and RMR were applied probabilistically in the back analysis of the tunnel design.Distributions of Q and RMR were developed for discrete points along the tunnel for prior and future tunnel alignments.Based on the probabilistic rock mass classifications, installed support systems were compared to actual support systems.Important differences between deterministic and probabilistic results were observed, including bias of deterministic results away from peak probabilistic results.Numerical models using rock mass properties based on probabilistic rock mass classifications were found to provide a good match to rock loads recorded during tunnel construction.This result suggests that forward modeling of tunnels using probabilistic rock mass classifications may provide a more realistic estimate of tunnel support requirements, costs, and risks.Current deterministic geotechnical baseline reports may be inadequate to provide a complete picture of a proposed project and may bias design towards a sub-optimal solution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".