Static and dynamic vehicle load identification with lane detection from measured bridge acceleration and inclination responses
Bibliographic record
Abstract
The effects from passing vehicle's load on bridge are divided into two categories: static effect and dynamic effect. The static effect usually indicates the pseudo-static responses of the bridge caused by the vehicle gross weight while the dynamic effect means dynamic responses due to vehicle and bridge dynamic properties and bridge pavement roughness. Both effects need to be properly evaluated because the vehicle static weight is a governing factor in determining bridge's fatigue life while the dynamic part of the vehicle load tends to amplify the bridge responses. In this paper, a method based on an extended Kalman filter is proposed to identify vehicle static and dynamic load only from responses recorded by portable accelerometers, together with their transverse position, which affects the identification of the loads. Even though only accelerometers are employed as sensors, the bridge vertical acceleration can capture the dynamic load components and the inclination obtained from the projection of the gravitational acceleration in the longitudinal direction can capture the low-frequency component. The feasibility of the proposed method is proved through numerical simulation and an experimental test on a bridge. This paper makes full use of three-axis accelerometers to estimate vehicle's static and dynamic load considering the vehicle's passing route, thus solving the nonlinear problem caused by vehicle's transverse position. Moreover, the proposed method eliminates the need of using other devices like displacement sensor or strain gauges to obtain low-frequency components.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".