Nonlinear elastoplastic formulation for tunneling effects on superstructures
Bibliographic record
Abstract
The paper presents a formulation for evaluating the effect of tunneling on existing buildings. The formulation involves the matrix condensation method to represent the response of a linear elastic building and macroelements to represent the nonlinear elastoplastic soil behavior. The formulation includes new features that allow interaction between macroelements, both through the soil continuum and the structure, to result in the final displacements of the foundations due to tunneling. One of the advantages of the formulation is its ability to incorporate a general input of a greenfield field displacement for the interaction analysis, allowing consideration of various tunneling scenarios. The formulation is evaluated by a comparison with a continuum-based solution obtained using the finite difference method. The formulation is then used to conduct a parametric analysis of tunneling–soil–superstructure interaction, considering three different approaches: (i) the suggested elastoplastic formulation, (ii) purely elastic analysis, and (iii) simplified analysis in which the foundations are forced to displace as a greenfield. It is shown that the vertical settlements of the foundations, due to tunneling, are the greatest when the first approach is considered. This is an outcome of the combined vertical and horizontal yielding, depicted in the formulation by the coupled yield function and plastic flow potential. Yet damage, which relates to differential settlement, appears to be smaller in the elastoplastic formulation.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".