A gradient based optimization procedure for finding axle weights in probabilistic bridge weigh-in-motion method
Bibliographic record
Abstract
Bridge weigh-in-motion (BWIM) consists of the use of sensors on bridges to assess the loads of passing vehicles. Probabilistic bridge weigh-in-motion (pBWIM) is an approach for solving the inverse problem of finding vehicle axle weights based on deformation information. The pBWIM approach uses a probabilistic influence line and seeks the most probable axle weights, given in situ measurements. To compute such weights, the original pBWIM employed a grid search, which may lead to computational complexity, especially when applied to vehicles with high number of axles. Hence, this note presents an improved version of pBWIM, modifying the manner in which the most probable weights are sought. Here, a gradient based optimization procedure is proposed for replacing the computationally expensive grid-search of the original algorithm. The required gradients are fully derived and validated in numerical examples. The proposed modification is shown to highly decrease the computational complexity of the problem.
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How this classification was reachedexpand
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 | 0.001 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".