Subsurface settlements of shield tunneling predicted by 2D and 3D constitutive models considering non-coaxiality and soil anisotropy: a case study
Bibliographic record
Abstract
Appropriate constitutive models and reliable excavation and support sequences are believed to be the major concern in using finite element (FE) analysis to simulate shield tunnel excavation. This paper presents systematic two-dimensional (2D) and three-dimensional (3D) FE analyses employing a number of constitutive models accounting for initial soil anisotropy and non-coaxial plasticity, as evidenced within site investigations from the Tsinghuayuan Tunnel of the Jing-Zhang high-speed railway in China. The aim is to assess the effects of both the initial soil anisotropy and non-coaxiality on longitudinal and transverse tunneling-induced surface settlements. It is shown that the excavation procedures combined with the degree of cross-anisotropy are key towards the accurate prediction of maximum vertical displacements from tunneling, matching field data. Knowledge of the initial soil strength anisotropy can further improve the shape prediction of the transverse tunneling-induced surface settlement troughs. When considering n = 0.6 and β = 0° in simulations, the transverse surface settlement trough obtained is almost coincident with monitored field data. Initial stiffness anisotropy used in the prediction of shield tunnel-induced surface settlements in sandy pebble soils does improve realism of results significantly. The maximum longitudinal settlement predicted by considering cross-anisotropy is larger than that predicted by its isotropic counterpart.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".