A systematic literature review of unmanned underwater vehicle-based structural health monitoring technologies
Bibliographic record
Abstract
The structural health of underwater infrastructure such as bridges, dams, and pipelines are constantly degrading due to aging, fatigue, unexpected loads, and environmental wear and tear. Historically, these structures have been inspected by human divers; however, the need for safe and cost-effective monitoring has fostered the development of unmanned underwater vehicles (UUVs) capable of performing subsea surveillance. This paper provides a concise and systematic review of emerging technologies and methodologies for deploying underwater vehicles to perform inspections. Literature is classified into two main groups: advancements to UUV designs and capabilities and advancements to instrumentation for underwater structural health monitoring. After a systematic review, the existing challenges to UUV development and implementation are discussed. Finally, recommendations for future areas of research are outlined. This systematic literature survey aims to provide researchers and practitioners with a holistic outlook on the current state and future trends of UUV-based infrastructure inspection. • This paper provides a systematic review of the use of UUVs for underwater SHM. • Publications are classified and discussed into categories of type of drone systems and underwater data acquisition. • A discussion is provided on current challenges and future research directions. • This review demonstrates the usefulness of UUVs for autonomous underwater SHM.
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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".