Detecting, monitoring and modeling damage within the decision-making process in the context of managing bridges: a review
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
The expansion of transportation infrastructure and the aging and deterioration of its constituent elements make bridge maintenance management programs more expensive and complex. In this context, a bridge management system (BMS) has become a fundamental tool for managing and controlling the entire process involving the structures, from design, construction, operation, and maintenance. Information regarding bridges, inspection, and damage detection should be standardized and digitized for stakeholder access. The provided bibliometric analysis demonstrates that inspection, structural health monitoring (SHM), deterioration, damage detection, and decision-making are trending topics. These topics guided a comprehensive literature review bringing advances and discussing assessment quality, the ability to detect damage, and the most accurate and cost-effective intervention. Finally, the challenges and limitations of these topics are identified, and possible solutions to overcome these limitations are discussed.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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