Combining GNSS and accelerometer measurements for evaluation of dynamic and semi-static characteristics of bridge structures
Bibliographic record
Abstract
Abstract With the increasing number of long span bridges, real-time, accurate and continuous monitoring of their safety is important at present. This study investigates the combination of a global navigation satellite system (GNSS) and accelerometer for monitoring dynamic and semi-static characteristics of bridge structures. A field experiment was conducted with the integration of a GNSS and accelerometer. Considering the noise interference of GNSS monitoring, performance tests were first conducted in different environments to investigate the noise characteristics. Next, complete ensemble empirical mode decomposition with adaptive noise-wavelet packet (CEEMDAN-WP) algorithm was chosen for denoising, among which a double criterion based on the correlation coefficient and effective coefficient was proposed to sift the intrinsic mode functions. After the noise reduction process, structural dynamic displacements and modal frequencies were successfully extracted from the 50 Hz GNSS real-time kinematic (GNSS-RTK) and accelerometer data, in which the displacements presented a consistent trend and the first natural frequency was the same (i.e. 0.369 Hz). Structural semi-static characteristics were evaluated by using 1 Hz (RTK), post-processed kinematic, and precise point positioning data. With reference to relevant specifications, the structural failure probability of the bridge in the vertical direction was calculated to be 0.4319. The results indicate that GNSS-RTK is reliable in monitoring structural dynamic and semi-static displacements of the bridge. Additionally, the proposed improved CEEMDAN-WP with double criterion is effective for background noise reduction. In addition, there may be some non-adequate behaviors, such as heavy traffic and vehicle overload, leading to the critical operation of the bridge.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".