Ability of three different soil constitutive models to predict a tunnel’s response to basement excavation
Bibliographic record
Abstract
Many constitutive models are available nowadays to predict soil–structure interaction problems. It is sometimes not very easy for engineers to select a suitable soil model to carry out their design analyses in terms of complexity versus accuracy. This paper describes the application of three constitutive models to back-analyse a well-instrumented centrifuge model test, in which the effect of basement excavation on an existing tunnel was simulated. These three models include a linear elastic – perfectly plastic model with the Mohr–Coulomb failure criterion (called MC model), a nonlinear elastic Duncan–Chang model (DC), and a hypoplastic model (HP), the last of which can capture the state-, strain-, and path-dependent soil stiffness even at small strains and path- and state-dependent soil strength. By comparing with measured data from the centrifuge model test, it is found that the HP model yielded the best predictions of tunnel heave among the three models. Not only the gradient, but also the magnitude of tunnel heave is well predicted by this HP model. This can be explained by the fact that the HP model can capture the state-, strain-, and path-dependent soil stiffness even at small strains and path- and state-dependent soil strength, but not the MC and DC models. However, all three models underestimated the change in tunnel diameter and the maximum tensile bending strain in the transverse direction.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".