Fiducial marker-based decentralized computer vision for structural modal identification
Bibliographic record
Abstract
• Introduces novel vision-based approach for decentralized vibration measurement in structural health monitoring. • Integrates multiple cameras in a decentralized setup for full-field measurement, capturing high-density spatial data in high resolution. • Uses fiducial markers as inexpensive virtual sensors for 3D time series extraction via camera calibration and pose estimation. • Offers contactless and remote measurements with high spatial density, reducing cost and time compared to traditional methods. • Validated through rigorous laboratory tests on building and beam models, and field tests on a truss bridge. Due to the advancement in optics and computer vision, the implementation of the vision-based technique is extensively being investigated for structural health monitoring. Compared with traditional contact sensing measurements, computer-vision technology offers contactless and remote measurements with high spatial density at low cost and instrumentation time. This study proposes an innovative contactless vision-based decentralized vibration measurement technique, where the fiducial marker is utilized as an inexpensive virtual sensor to extract structural vibration measurements using 3D pose estimation through camera calibration. Once the vibration measurements are extracted, covariance-driven stochastic subspace identification is employed due to its robustness for effective mode decomposition and noise reduction capabilities. This paper enables the extraction of 3D time series without deploying a stereo camera system and combines the multiple fields of view of different regions of interest from various cameras in a decentralized manner to capture high-density and high-resolution spatial data for full-field measurement. Two laboratory tests were conducted on a lab-scale building model and a lab-scale beam model to validate the robustness and effectiveness of the proposed methodology. Following the laboratory validation, field tests on a full-scale truss bridge were performed to demonstrate the efficacy of the proposed technique. The relative error in the estimation of the modal frequencies in lab-scale experimentation is <2 %, whereas, for the field study, it is <5.5 %, considering the working distance between the camera and field bridge is over 30 m. The modal assurance criterion (MAC) between the extracted mode shapes is also estimated, and the average MAC values for lab-scale building and lab-scale beam models are 98.61 % and 97.28 %, respectively. The proposed technique has proven to capture the minuscule vibration of the structure at a considerable distance between the structure and the vision-based system, including a detailed comparative study between the vision-based system and accelerometers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".