Laser-Based Field Measurement for a Bridge Finite-Element Model Validation
Bibliographic record
Abstract
In bridge engineering, laser-based measurement techniques show promise in assisting field tests due to their noncontact features. A case study of using laser-based remote sensing to help collect data during in situ testing for a bridge finite-element (FE) model validation is reported in this paper. The skewed two-span bridge in this study was constructed with nine high performance steel girders in two phases. A three-dimensional (3D) FE model of the bridge superstructure was developed based on the information provided by the design files. Various field tests were performed to validate the model: (1) light detection and ranging (LiDAR) scanning, (2) static truck load tests, and (3) laser Doppler vibrometer testing. The LiDAR scanner collected geometrical information of the actual bridge. It was also used to measure girder deflections during load testing. The fundamental frequency of the bridge vibration was obtained by using a laser Doppler vibrometer (LDV). In situ dynamic and static measurements were compared to the FE model results, thus offering validation of the analytical predictions. Such analysis of the bridge superstructure serves as a baseline for post construction investigations, with important implications especially for the long-term structural health monitoring of the system as a whole.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".