Calibration and Validation of S3F Sensor for Measuring Normal and Shear Stresses in Soil
Bibliographic record
Abstract
ABSTRACT Innovative sensors can provide new capabilities to monitor and understand the behavior of soil, rock, and geo-structures and help geotechnical engineers make informed decisions about the construction and maintenance of geo-structures. This study introduced, calibrated, and validated one such sensor, the Surface Stress Sensitive Film (S3F) point sensor, for both normal and shear stress measurements in soil and along the soil–structure interface. The measurements of the S3F sensor rely on the deformation of an elastic film that is monitored by a magnetic floating element embedded in the elastic film and a Hall effect sensor. This sensor provides measurements of the 3-D deformation of the film, which are converted to normal and shear stresses using an a priori calibration. The calibrations of the S3F sensor were performed considering the effect of the loading areas, loading and unloading conditions, and soil particle sizes. Then, the performance of the S3F sensor to measure the normal stresses in soil and shear stresses at the soil–wooden block interface under static tension and pull-out conditions was evaluated. It was found that the normal stress calibration curves depended on the sizes of the loading areas because of the stiff housing boundary effect. However, the shear stress calibration curves were independent of the loading areas. The S3F sensor showed an ability to measure normal stresses in three different types of soils, including two silica sands from Ottawa, Illinois, with particle sizes ranging between sieve No. 20 and 30 (Ottawa 20/30 sand) and sieve No. 50 and 70 (Ottawa 50/70 sand) and finely ground silica silt (Sil-Co-Sil). The S3F sensor also showed an ability to measure the shear stresses at the soil–structure interface, which match well with the theoretical shear stresses. The S3F sensor has potential for stress measurements at the soil–structure interfaces in foundations, tunnels, pipes, and retaining systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| 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 teacher head, 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".