Settlement control in large span tunnels under shallow over burden: a case study of the Arash-Esfandiar-Niayesh tunnel
Bibliographic record
Abstract
Abstract Settlement control is pivotal in the construction of large span tunnels, particularly when situated under shallow overburden. The importance of settlement control in the context of the Arash-Esfandiar-Niayesh tunnel project, a multi section tunnel designed to facilitate two-way traffic in Municipality of Tehran District 3, is studied. The tunnel, which possesses an overburden of 4–15 m, faces unique challenges such as low overburden, urban location, and proximity to tall structures. By integrating the New Austrian Tunneling Method (NATM), this paper examines the effects of various support systems on construction of the tunnel project. Two critical sections are analyzed, and the settlement control strategies are examined according to a combination of various support elements. Analysis and numerical modeling based on PLAXIS 2D finite element code revealed the impact of these support systems on ground settlements on the structural and geotechnical stability. They had a substantial impact of tunnel geometric shape on sustainability and stress distribution. The inadequacy of common support systems, especially in tunnels with a horseshoe section, is highlighted. The impact of removing temporary lattice girderson surface settlements emphasizes the importance of settlement monitoring. Field data, including a maximum vertical displacement of 2.6 cm on the ground and in the tunnel, validate the numerical analysis results. Despite the challenges, the consistency between numerical and instrument data underscores the effectiveness of settlement control strategies.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".