Measurement of Deformations by MEMS Arrays, Verified at Sub-millimetre Level Using Robotic Total Stations
Bibliographic record
Abstract
Measurement of sub-millimetre-level deformations of structures in the presence of ambient temperature changes can be challenging. This paper describes the measurement of a structure moving due to temperature changes, using two ShapeAccelArray (SAA) instruments, and verified by a geodetic monitoring system. SAA is a geotechnical instrument often used for monitoring of displacements in soil. SAA uses micro-electromechanical system (MEMS) sensors to measure tilt in the gravity field. The geodetic monitoring system, which uses ALERT software, senses the displacements of targets relative to control points, using a robotic total station (RTS). The test setup consists of a central four-metre free-standing steel tube with other steel tubes welded to most of its length. The central tube is anchored in a concrete foundation. This composite “pole” is equipped with two SAAs as well as three geodetic prisms mounted on the top, in the middle, and in the foundation. The geodetic system uses multiple control targets mounted in concrete foundations of nearby buildings, and at the base of the pole. Long-term observations using two SAAs indicate that the pole is subject to deformations due to cyclical ambient temperature variations causing the pole to move by a few millimetres each day. In a multiple-day experiment, it was possible to track this movement using SAA as well as the RTS system. This paper presents data comparing the measurements of the two instruments and provides a good example of the detection of two-dimensional movements of seemingly rigid objects due to temperature changes.
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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 | 0.000 | 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) | 0.002 | 0.001 |
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".