Material property predictions based on GPR attributes: Testing on concrete pedestrian bridge
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Non-invasive subsurface investigations, particularly ground penetrating radar (GPR), are well adapted to characterizing and understanding geological or anthropogenic features. Estimates of the material and physical properties of these features are available via methods such as ultrasonic and seismic methods, but those existing techniques fall short for certain applications. New work with GPR is beginning to establish techniques for material characterization and quantitative estimation of material properties. By combining attribute analysis of GPR data (based on image processing and seismic data analyses) with supervised learning on a new data set of concrete properties, we create new predictive models for compressive strength, porosity, and density of concrete samples. This work applies those lab-based models to predict the material properties of a reinforced concrete pedestrian bridge using GPR scans of the deck. The models are successful at predicting compressive strength, density and porosity. Though this particular application presents certain challenges, including applying the model to field data collected with a different GPR antenna than the lab data, the results are a promising step toward wholly noninvasive material property estimates using GPR.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it