AI-guided bridge deck inspection using vehicle-mounted infrared imaging and ultrasound tomography
Bibliographic record
Abstract
Traditional bridge deck inspections often involve manual labour and data recording, which can be time-consuming and error-prone. Infrared (IR) has strong potential to improve inspections. However, manual IR data processing can also be time-consuming. This paper presents a methodology that integrates IR imaging, an Artificial Intelligence (AI) model and Ultrasound Tomography (UT) to streamline and enhance inspections. The approach begins with vehicle-mounted IR imaging for rapid, large-scale scanning of bridge decks to identify potential concerns. Unlike conventional methods and earlier AI-integrated studies relying on pre-processed IR data, this approach uses processed IR data to label unprocessed images, which are then used to train a Grounding DINO AI model. The trained model autonomously detects and localises suspicious regions directly from raw IR images, eliminating labour-intensive processing and enabling real-time defect detection. UT is subsequently employed to provide detailed analysis of flagged areas, offering insights into damage types such as delamination, spalling and defect depth. The AI model’s precision, around 90%, is evaluated against ground truth from processed IR data. Integrating these technologies, the proposed method offers great potential to accelerate inspections, improve defect localisation with global coordinates and support efficient maintenance planning.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".