Corrosion Assessment of Reinforced Concrete Structures using Ground-Penetrating Radar
Bibliographic record
Abstract
Corrosion affecting reinforced concrete structures is a critical concern in civil engineering in terms of structural integrity, especially in critical infrastructure, safety risks to users, and long-term durability and safe operation over time, as well as for environmental impact and financial implications.Within this context, early detection of corrosion in reinforced concrete structures is crucial for time intervention, enabling preventive maintenance and anticipating future deterioration.This work proposes the Ground-Penetrating Radar (GPR) as a recognized method for assessing corrosion in concrete structures.First, an overview of the effects of corrosion on the GPR signal, and how it can be detectable from the GPR data, is presented.Next, two different case studies are addressed, including the evaluation of a precast bridge deck in Galicia, and the unique structures of the UNESCO World Heritage Site of Park Güell in Barcelona.New trends on the development of robots to improve accessibility and autonomous data collection are also commented, as well as the use of artificial intelligence for automatic corrosion detection and the possibilities for data digitization into interoperable Building Information Modelling (BIM) and digital twin environments.Finally, it should be highlighted that identifying corrosion at an early stage allows engineers to take proactive measures to prolong the lifespan and serviceability of structures.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".