Bibliographic record
Abstract
Condition ratings of bridge components in the Federal Highway Administration (FHWA)’s Structural Inventory and Appraisal database are determined by bridge inspectors in the field, often by visual confirmation or direct- contact sounding techniques. However, the determination of bridge condition ratings is generally subjective depending on individual inspectors’ knowledge and experience, as well as varying field conditions. There are also limitations to access, unsafe working conditions, and negative impacts of lane closures to account for. This paper describes an alternative method to obtaining informative and diagnostic inspection data for concrete bridge decks: mobile nondestructive bridge deck evaluation technology. The technology uses high- definition infrared and visual imaging to monitor bridge conditions over long-term (or desired) intervals. This combination of instruments benefits from rapid and large-scale data acquisition capabilities. Through its implementation in Japan over the course of two decades, the technology is opening new possibilities in a field with much untapped potential. Findings and lessons learned from our experience in the states of Virginia and Pennsylvania are described as examples of highway-speed mobile nondestructive evaluation in action. To validate the accuracy of delamination detection by the visual and infrared scanning, findings were proofed by physical sounding of the target deck 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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".