Material property predictions based on GPR attributes: Testing on concrete pedestrian bridge
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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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) | 0.001 | 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 | 0.001 | 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".