Recent Advancements and Future Trends in Indirect Bridge Health Monitoring
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
Bridges hold an imperative role in the transportation network and infrastructure. Continuous monitoring of their condition is crucial for the efficient operation of transportation facilities. Conventional bridge monitoring has relied on direct sensor instrumentation on the bridge to obtain the bridge response. Indirect bridge health monitoring (iBHM) leverages the moving traffic over the specific bridge of interest. The benefit of iBHM lies in the fact that bridge instrumentation is no longer required since the moving vehicle is instrumented with sensors. The collected data can be used to identify the dynamic characteristics of the bridge. Additionally, the method can be used to detect damage using the information of the vehicle bridge interaction. This paper systematically reviews the recent research progress in iBHM, and the review is organized based on four main groups, namely single test vehicles, tractor-trailer vehicles, crowdsourced/smartphone monitoring, and contact point (CP) response. The primary classification is further divided according to the nature of the investigation, which includes theoretical and numerical investigations, laboratory tests, and full-scale validations. After a concise and systematic review, the existing challenges and future recommendations are outlined. It is anticipated that this review will provide valuable guidance for researchers and practitioners of bridge engineering to understand better the evolution, development, and future trends of iBHM.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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