Corrosion Assessment of Reinforced Concrete Structures using Ground-Penetrating Radar
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
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 GroundPenetrating 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 it