Deformational behaviors of existing three-line tunnels induced by under-crossing of three-line mechanized tunnels: a case study
Bibliographic record
Abstract
This research presents monitoring data from a complicated case: the under-crossing of new three-line tunnels beneath existing three-line tunnels. The existing tunnels were monitored by electric level and wireless sensing network (WSN). By analyzing the monitoring data, the mutual influence patterns of the new three-line tunnels were identified, and a modified version of Peck’s formula was proposed. Additionally, a correlation between settlement data from the low-frequency electric level and tilt change data from the high-frequency WSN was established. This correlation provided a detailed understanding of the deformation process of the existing tunnels during the under-crossing. The results revealed that the deformation patterns of the existing three-line tunnels during the under-crossing process are similar and could be categorized into three stages. For each stage, a support vector machine prediction model was developed and interpreted using the Shapley Additive explanation method to rank the degree of influence of each construction parameter on the settlement. Grouting volume, shield position, pushing jacks’ pressure, and earth pressure were the main factors influencing the settlement and their importance varied in different stages due to the different relative position of the shield machine to the existing tunnels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".