Comparative Performance Test of an Inclinometer Wireless Smart Sensor Prototype for Subway Tunnel
Bibliographic record
Abstract
Structural health monitoring of operating subway tunnel has become a new challenge to engineers. In order to make structural monitoring cheaper, smarter and more ecient, new technologies such as micro- electro-mechanical system (MEMS) sensors and wireless sensor network (WSN) have recently been introduced to compensate the defects of conventional methods. A wireless MEMS inclinometer prototype was developed by Tongji University. Many adaptability tests on both developed and commercial sensors are carried out in labora- tory under the same condition. The comparison shows that the inclinometer prototype has a higher resolution, larger range, lower cost, better electric property and less temperature disturbance than the commercial product but is slightly bigger in size. Then, a full size shield tunnel segment deformation experiment is carried out. Tilt angles at some particular locations are measured by the MEMS inclinometers so as to calculate the global deformation of the segment. Conventional displacement meter is used to verify the calculated results. The feasibility of utilizing inclination of segments to evaluate convergence deformation of shield tunnel is studied in this paper. The geometry method is adopted in analysis, and the convergence deformation of shield tunnel is assumed as a function of segments inclination. The experiment shows a solid feasibility of inclinometers being used to monitor convergence displacement of shield tunnel. Problems discovered in the research and future work are also discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".