Assessment of Ground Penetrating Radar for Pyrite Swelling Detection in Soils
Bibliographic record
Abstract
Pyrite swelling in soils below buildings is a major issue. It leads to severe deformations in floor foundations. A survey is carried out at a selected site in the city of Laval, Quebec, to assess the usefulness of ground-penetrating radar (GPR) to detect deformations that may be indicative of the presence of pyrite. Four soil samples are taken from the aforementioned site to determine the soil type below the concrete slab. The results indicate the presence of limestone, moor clay, and shale sediments, which are prone to pyrite swelling. The GPR data were collected using the GSSI SIR 4000 with a high frequency antenna and processed using RADAN software. The GPR data indicate the presence of severe deformation in many locations of the concrete slab. The most important wave reflections indicative of pyrite swelling are the rebar reflections, showing interesting pushed-up and dropped-down reflections. These reflections appear in two forms. The first is the attenuated reflections that may occur due to pyrite-rich materials. The second is the high amplitude reflections that occur because of the air void, which can be formed due to heaving the concrete slab because of pyrite swelling. As a result, GPR appears to be an effective method for assessing and mapping the effect of pyrite swelling below concrete slabs. Doi: 10.28991/CEJ-2024-010-03-05 Full Text: PDF
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".