Monitoring and assessment of a cross-passage twin tunnel long-term performance using wireless sensor network
Bibliographic record
Abstract
The monitoring and assessment of ageing underground tunnels is critical to ensure their serviceability, stability, and safety as arteries for a transport network in the long term. This paper first comprehensively reviewed the long-term tunnel performance monitoring case studies, aimed at highlighting the limited field monitoring data and identifying research gaps. It was found that previous studies largely focused on the performance of single tunnel sections subject to short-term disturbances (e.g., adjacent excavation), whereas limited efforts concentrated on the long-term performance of twin tunnels, let alone those with cross passages, under the influence of deteriorations. To this end, a Wireless Sensor Network (WSN) was deployed at a critical vehicle cross passage (VCP) twin tunnel section of Dublin Port Tunnel to monitor its long-term ageing performance with time, in addition to the existing long-term water leakage and lining crack monitoring. The evolvement of lining crack and water leakage since 2010 indicated the progressive deteriorations of the monitoring section, and the deployed WSN monitoring of lining inclination demonstrated a robust sensor deployment layout and monitoring plan for (quasi) real-time monitoring for a confined underground cross passage twin tunnel network. An analytical solution was proposed to convert tunnel inclination to horizontal deformation, with the converted measurements suggesting that even more than one decade after construction, both twin tunnels are still moving horizontally towards the VCP centreline in the long term, primarily due to twin tunnel interaction. Along tunnel longitudinal direction, the closer to the VCP, the greater tunnel deformation rate is, revealing the effect of cross passage on tunnel differential longitudinal behaviour, in agreement with hypotheses and numerical results in previous studies. The field observations were believed to be attributed to the two mechanisms which are hydro-geological degradation of the surrounding ground and hydro-mechanical deterioration of the tunnel, where the correlation between tunnel deformation and deteriorations was detailed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".