Internet of Things (IoT) in Structural Health Monitoring: A Decade of Research Trends
Bibliographic record
Abstract
Structural Health Monitoring (SHM) is important for the safety and performance of civil infrastructure.With IoT, the SHM paradigm is changing; real-time wireless sensors capture and transfer data directly to data processing centres, eliminating physical wiring.IoT integration enables more effective, continuous, and responsive structural monitoring in real-time.Although there are many publications in this field, few comprehensive surveys have conducted scientific analyses.This paper presents bibliometric and scientometric analysis methods to see how research progress on wireless Internet of Things (IoT) technology is applied in SHM.Over the past ten years, 170 Scopus-based publications have been evaluated to achieve this goal.Annual trends, active journals, top researchers, research hotspots, nation involvement, and keyword emergence were all noted in the review.The data reveals a marked upsurge in research activity trends, with the US playing a prominent role.Clustering visualisation with VOSviewer software was used to classify programs into various clusters and identify the scope of applications and their relationships through link strength.The findings provide a comprehensive picture of the utilisation of the Internet of Things for SHM, highlighting trends and can serve as pointers/knowledge to assist researchers in future research.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".