Evaluation of geological hazards along the Karaj water conveyance tunnel using multiple approaches and GIS
Bibliographic record
Abstract
The prediction of geological hazards prior to tunnel excavation is of paramount importance, as understanding these hazards is fundamental in tunnel design, route selection, drilling technology choice, maintenance, and the construction of associated structures. The most significant geological challenges that may occur during and after tunnel excavation include groundwater inflow, tunnel face squeezing, convergence of tunnel walls, the collapse of fractured zones, and rockburst. These hazards can lead to unforeseen costs, operational disruptions, and schedule delays. Therefore, comprehensive assessments of geological hazards are essential before commencing underground projects to facilitate informed design decisions. In this study, we apply various analytical, numerical, empirical, and semi-empirical methods to estimate and assess geological hazards associated with the Karaj water conveyance tunnel, Iran. These hazards include water inflow into the tunnel and the tunnel's susceptibility to squeezing and rockburst. The identified risks along the Karaj water conveyance tunnel route are then classified within a GIS environment. Maximum water inflow into the tunnel corresponds to sections having high rock squeezing, primarily in fractured and faulted zones. These zones, along with sections of the tunnel characterized by low rock mass quality but high rock mass yield, exhibit the highest risks for potential squeezing and rockburst.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".