Assessment of twin tunnelling induced settlement and load transfer mechanism of a single pile in dry sand
Bibliographic record
Abstract
This study conducted both three-dimensional physical model tests and numerical back-analyses to examine the performance of a single pile subjected to twin tunnelling beneath the pile toe in dry sand. Moreover, numerical parametric analyses were carried out to assess the impact of working load level and tunnel volume loss on pile behaviour. The findings revealed that the first tunnelling induced a 1.9% dp (pile diameter) settlement in the pile when the pile toe was 0.5 D (tunnel diameter) above the twin tunnels, which had a 1.0% volume loss. The settlement was accompanied by an upward load transfer mechanism. The second tunnelling had a reduced impact and induced only 76% of the settlement caused by the first tunnelling. The study also showed that tunnelling-induced shearing behaviours weakened beneath the pile toe with a decrease in working load, resulting in reduced pile head settlement. Moreover, the computed pile settlement increased by 178% when the tunnel volume loss increased from 0.5% to 2%. The findings of this study offer valuable data for the validation and calibration of numerical models. In addition, the outcome from this study provides useful insights into the prediction of the pile-soil-tunnel interaction when subjected to twin tunnelling.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".