Investigating the ability of high-rate GNSS-PPP for determining the vibration modes of engineering structures: small scale model experiment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 it