3D vision-based out-of-plane displacement quantification for steel plate structures using structure-from-motion, deep learning, and point-cloud processing
Bibliographic record
Abstract
In this paper, a novel accurate and economical 3D computer vision-based framework is proposed to quantify out-of-plane displacements of steel plate structures. First, a sequence of image frames of the steel plate structures of interest is collected. Second, using image association, structure-from-motion, and multi-view stereo algorithms, a 3D point cloud of the steel plate structures and their surroundings is created. Third, an efficient 3D object detection method based on convolutional neural networks is developed and implemented to identify the steel plate structures in the 3D point cloud. Last, the out-of-plane displacements of the steel plate structures are quantified using point cloud postprocessing algorithms. The proposed framework has been implemented on a steel plate damper and a full-scale steel corrugated plate wall panel, which are commonly used in structural and earthquake engineering applications. The results indicate the developed framework can successfully localize the steel plate components in the 3D scene and accurately quantify the out-of-plane structural displacements with an average accuracy of ∼1 mm. The implementation shows the proposed framework can accurately and efficiently quantify the out-of-plane displacements of steel plate structures in realistic engineering applications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".