An Evaluation of Real-Time Deformation Monitoring Using Motion Capture Instrumentation and Its Application in Monitoring Railway Foundations
Bibliographic record
Abstract
Abstract ABSTRACT:The purpose of this study is to evaluate the use of motion capture instrumentation to monitor the response of a railway embankment and the underlying soft peat mire foundation soils to freight train loading. Initial data sets were obtained from the motion capture system, called the ShapeAccelArray (SAA, Measurand Inc.), installed in a railway embankment. Review of the data sets from the site installation raised questions as to the ability of the SAA to provide accurate displacement measurements. Testing of the SAA in the laboratory confirmed that the output from the SAA system (inclusive of software) would not provide a true measurement of horizontal deformations during large cyclic motions. This inaccuracy was due to the magnitude of acceleration associated with the cyclical motion on the microelectromechanical systems (MEMS) accelerometers and the effect of this on the ability of the system to determine its shape. A method for determining the magnitude of cyclic displacement from the output of the MEMS accelerometers was developed from the laboratory testing data. This involved the double integration of the change in acceleration measured by the accelerometers to obtain a change in displacement. This method was applied to the data sets obtained from the field installation to obtain a profile of cyclic displacement with depth.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.001 | 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".