Investigating the ability of high-rate GNSS-PPP for determining the vibration modes of engineering structures: small scale model experiment
Bibliographic record
Abstract
This study evaluates the performance of the Precise Point Positioning method using Global Navigation Satellite System measurements (GNSS-PPP) for monitoring vibration modes of shear type buildings excited by harmonic ground motions and hammer tests. For experimental testing, the shear type lumped-mass building system is represented by a specially designed metal frame model, resembling a three story building, which was excited on a small scale shaking table. The excitation protocols applied were harmonic motions with different frequencies and amplitudes. The metal model has special deformation plates at the column tips to prevent the nonlinear rotations and out-of-plane motions for the entire system. The fundamental vibration periods of the model structure were computed by a Finite Element Mathematical (FEM) model, which were compared with the position variations determined by GNSS-PPP. Two GNSS receivers were mounted on top of the model structure on the line perpendicular to the motion axis to measure the rotation motion. The GNSS data comprised dual-frequency observations with a 10 Hz sampling rate. GNSS-derived positioning was obtained by processing the data using a post-mission kinematic PPP method with fixed phase ambiguities. Analysis of the characteristics of the vibration frequencies showed that the high-rate GNSS PPP method can capture the frequencies of first motion mode of shear type structural response when compared with the FEM output. Results demonstrate the efficiency of the high-rate GNSS PPP method in monitoring first motion mode of a natural frequency.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".