Development of muon scattering tomography for detection of reinforcement in concrete
Bibliographic record
Abstract
Abstract Inspection of ageing, reinforced concrete structures is a world-wide challenge. Existing evaluation techniques in civil and structural engineering have limited penetration depth and do not allow to precisely ascertain the configuration of reinforcement within large concrete objects. The big challenge for critical infrastructure (bridges, dams, dry docks, nuclear bioshields etc.) is understanding the internal condition of the concrete and steel, not just the location of the reinforcement. Muon scattering tomography is a non-destructive and non-invasive technique which shows great promise for high-depth 3D concrete imaging. Here a method is presented to locate reinforcement meshes placed in a large-scale concrete object. A reinforcement mesh was simulated as two layers of 2 m long bars, forming a mesh. Two layers of the mesh were placed at several distances from each other inside a large concrete block. Previously, we have shown that using our autocorrelation technique for single meshes inside the concrete and using only one week worth of data taking, bars with a diameter of 7 mm and larger, could easily be detected for a 10 cm mesh spacing. The signal for 6 mm diameter bar exceeds the background and becomes very clear after two weeks of data taking. Here we show that we can detect the vertical positions of two mesh layers inside the concrete. This is a very important result for non-destructive evaluation of civil structures.
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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 | 0.000 | 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".