A unified empirical method for predicting both vertical and horizontal ground displacements induced by tunnel excavation
Bibliographic record
Abstract
An empirical model for describing the soil movements induced by tunneling is proposed, then the mathematical relationship between horizontal displacement and vertical displacement is obtained. By analyzing 25 sets of data from field engineering and 35 sets of data from model tests, the formula for the maximum settlement Sv, max( z) is optimized to adapt to different ground conditions. The Modified Gaussian function is developed by introducing the existing width coefficient of the settlement trough i( z) and the optimized Sv, max( z) to describe the surface and subsurface vertical displacement. Subsequently, the formula for the horizontal displacement is derived based on the Modified Gaussian function. H( z) representing the position of the oriented point of the soil movement is a variable in the formula for horizontal displacement. Based on measured results, a logarithmic function and a quadratic polynomial function are proposed to describe the variation of H( z) with depth in the clay stratum and sand stratum, respectively. Then, the rationality of the proposed method is validated by 4 sets of in-situ data and 4 sets of test data. Finally, the implementation process of the proposed method is illustrated, and the inversion results of the ground displacement field in Beijing Metro Line 12 are presented.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".