Bridge model updating using distributed 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
One of the challenges of managing bridge infrastructure is developing numerical models that can be used to accurately assess highly redundant bridge systems. One way to refine the model is to use sensor data to perform model updating. However, conventional sensors provide limited data with which to update the model, given the many degrees of freedom associated with indeterminate structures, resulting in a large potential error. Distributed sensing technologies such as digital image correlation and fibre optic strain sensors have the potential to provide more extensive data sets for model updating. This paper presents a case study of a reinforced concrete bridge that was modelled numerically to predict the bridge performance. The bridge was then load tested, and distributed sensor data were acquired. Using the sensor data, the numerical model was updated and refined estimates of the bridge behaviour were obtained. The initial and final models produced estimates of bridge behaviour that differed by an order of magnitude, illustrating the importance of sensor data for some bridge assessments. Additionally, the model indicated that the stiffness of the bridge had increased with time owing to an increase in the elastic modulus of the concrete and the development of compressive stresses.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 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 it