Effects of foundation pressure and building stiffness on tunnel-separated footing interaction
Bibliographic record
Abstract
The displacements and deformations of buildings with separated footings caused by tunneling may be significant and could damage the structures. This paper numerically investigates the influences of building stiffness, geometry, and foundation pressure on the deformation of a two-story elastic framed building due to tunneling in sand. An advanced soil constitutive model known as hypoplastic model was calibrated and adopted to simulate the sand behavior. Results show that the roles of building stiffness and foundation pressure on the footing displacements due to tunneling are significant, particularly for a larger tunnel volume loss. An increase in building stiffness reduces both the vertical and horizontal displacements of footings, while a greater foundation pressure primarily increases footing settlements. The influences of building stiffness and foundation pressure on building shear distortion are considerable, while their impacts on panel horizontal strains are minor for the investigated parameter ranges. The results also suggest the potential use of greenfield results as a conservative estimation of the distortion of buildings with separated, embedded footings when subjected to tunneling-induced displacements. Modification factors for shear distortion and horizontal strains are presented and show good agreement with the empirical centrifuge-derived envelopes for buildings resting on the soil surface.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".