Calibration and Use of S3F Sensor for Normal and Shear Stress Measurements in Soil
Bibliographic record
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 along the soil-structure interface. The measurements of S3F sensor rely on the deformation of an elastic film that is monitored by a floating element embedded in the elastic film. This sensor provides measurements of the 3D deformation of the film which are converted to normal and shear stress measurements 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 accurately measure the normal stresses in the soil tank 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 due to the stiff housing boundary effect. However, the shear stress calibration curves were independent of the loading areas. The S3F sensor showed its ability to measure normal stresses in three different types of soils (Ottawa 20/30 sand, Ottawa 50/70 sand, and Sil-Co-Sil silica silt) and to measure the shear stresses at the soil-structure interface, which match well with the theoretical values of normal and shear stresses. The S3F sensor has the potential for stress measurements for soil-structure interactions in shallow and deep foundations, tunnels, buried pipes, and retaining systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.001 | 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".