DYNAMIC CONCRETE BEAM DEFORMATION MEASUREMNET WITH 3D RANGE CAMERAS
Bibliographic record
Abstract
Abstract. Concrete beams are used to construct bridges and other structures. Due to the traffic overloading or the decaying state of structures, deformation of bridges or other structures occurs frequently. Therefore, the requirement to measure concrete beam deformation, as integral components of structures, is well recognized. Many imaging techniques such as digital cameras, laser scanners and range cameras have been proven to be accurate and cost-effective methods for large-area measurement of deformation under static loading conditions. However, for obtaining useful information about the behaviour of the beams or monitoring real-time bridge deformation, the ability to measurement deformation under dynamic loading conditions is also necessary. This paper presents a relatively low-cost and high accuracy imaging technique to measure the deformation of concrete beams in response to dynamic loading with range cameras. However, due to the range camera measurement principle, target movement could lead to motion artefacts that degrade range measurement accuracy. The results of simulated and real-data investigation into the motion artefacts show that the lower sampling frequency leads to the more significant motion artefact. The results from real data experiments have indicated that periodic deformation can be recovered with sub-millimetre accuracy when the 3 Hz and 4 mm amplitude target motion is sampled at a rate of least 20 Hz and with 31 MHz range camera modulation frequency. When the modulation frequency is 29 MHz, the best sampling frequency is 20 Hz to keep the error under sub-millimetre.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".