Non-intrusive bridge weigh-in-motion: integrating geophones and strain sensors for accurate vehicle characterization.
Bibliographic record
Abstract
This study introduces an innovative Bridge Weigh-in-Motion (BWIM) approach, utilizing a geophone, a novel sensor in the field of Structural Health Monitoring (SHM). Vehicle overloading poses a serious threat to bridge safety and service life. Loaded vehicles exert excessive stress on bridge decks, road pavements, and girders, leading to accelerated degradation of bridge structural components. Therefore, accurate information regarding real traffic loads, especially heavy vehicles, is critical for assessing bridge health. The proposed BWIM system combines geophones and strain sensors to accurately determine axle loads, axle spacing, and Gross Vehicle Weight (GVW) in regular traffic flow. The research methodology consists of bridge span instrumentation, data acquisition, processing, storage, and analysis, detailing the methods for extracting vehicle characteristics from measured bridge responses. Validation is done with field experiments on a real instrumented bridge in Winnipeg, Canada. This study focuses on loaded trucks. Velocity measurements exhibited an error range of -5% to 3.8%, with a confident 95% interval of -0.4% to 0.54% and an R2 value of 0.95, based on a sample of 64 vehicles. GVW calculations demonstrated an error range of -4.6% to +3.2%, and 95% confidence interval of -2.7% to 3.2%, derived from 6 runs of known GVWs. Axle detection accuracy was 95%, assessed across a sample of 41 trucks exceeding 150 kN in GVW. Axle spacings and loads were calculated in the error ranges of -10.52% to 7.8% and -4.97% to 10.48%, respectively. Confidence intervals for these metrics ranged from -2.4% to 3.2% and 1.05% to 8.6%, respectively. This study offers a contribution to the domain of SHM and Civionics, providing a reliable solution for axle detection of loaded trucks and assessing real traffic loads on instrumented bridges.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".