Enhanced Bridge Weigh-in-Motion System Using Hybrid Strain–Acceleration Sensor Data
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
In this study, a novel acceleration-based vehicle identification method is employed within a hybrid bridge weigh-in-motion (BWIM) system in which the traditional strain-based BWIM system is augmented with an array of accelerometers. The implementation of such a system is discussed through a full-scale case study arterial highway bridge in the province of New Brunswick, Canada. The accuracy of the proposed vehicle identification method was studied in detail using an extensive set of field study data. To achieve this, a systematic evaluation of existing methods for velocity estimation and axle identification was conducted, evaluating the effects of vehicle direction, lane position, vehicle velocity, and vehicle configuration. The methods were compared based on the sensor signal characteristics, the velocity estimation techniques, axles detection methods, and the effects on gross vehicle weight (GVW) calculation. From this study, it was found that the proposed hybrid system resulted in more accurate velocity estimation, axle identification, and ultimately better GVW estimation.
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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 | 0.001 | 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 | 0.000 | 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