Structural Health Monitoring of Precast Concrete Box Girders Using Selected Vibration-Based Damage Detection Methods
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
Precast, prestressed concrete box girders are commonly used as superstructure components for short and medium span bridges. Their configuration and typical side-by-side placement make large portions of these elements inaccessible for visual inspection or the application of nondestructive testing techniques. This paper demonstrates that vibration-based damage detection (VBDD) is an effective alternative for monitoring their structural health. A box girder removed from a dismantled bridge was used to evaluate the ability of five different VBDD algorithms to detect and localize low levels of spalling damage, with a focus on using a small number of sensors and only the fundamental mode of vibration. All methods were capable of detecting and localizing damage to a region within approximately 1.6 times the longitudinal spacing between as few as six uniformly distributed accelerometers. Strain gauges configured to measure curvature were also effective, but tended to be susceptible to large errors in near support damage cases. Finite element analyses demonstrated that increasing the number of sensor locations leads to a proportional increase in localization accuracy, while the use of additional modes provides little advantage and can sometimes lead to a deterioration in the performance of the VBDD techniques.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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