Accuracy Assessment of Horizontal Displacement Determination of Engineering Structures Using Modern GNSS Methods
Bibliographic record
Abstract
Objective. The purpose of this work is to assess the accuracy of determining horizontal displacements using modern GNSS methods by processing static observation files of different durations. To achieve this goal, both absolute (PPP) and relative (differential) data processing methods implemented in modern software packages were used. Methodology. To assess the accuracy, a metrological approach was used with a comparison of the results obtained with the value of the reference displacement, which was controlled by a mechanical micrometer with the appropriate accuracy. The standard deviation was used as the main statistical indicator of the dispersion of the results. The experimental displacement of the GNSS receiver antenna in the horizontal plane was recorded with an accuracy that is an order of magnitude higher than the accuracy of GNSS determinations. Results. The accuracy of determining millimeter-level displacements when processing data with different software solutions was investigated: the CSRS-PPP online service (Canada); Novatel GrafNav software (PPP and differential modes with accurate products); Trimble Business Center (differential processing using onboard orbits). The highest accuracy for 2-hour observations was shown by the differential method using precise orbital products. Absolute methods showed different levels of accuracy: from centimeter to millimeter – depending on the selected complex. All estimates are based on standard deviation indicators, the results are presented in the form of tables and graphs. Scientific novelty and practical significance. The paper proposes an experimental methodology for assessing the accuracy of determining horizontal displacements by GNSS methods using metrological control. The results obtained can be used to optimize the duration of GNSS sessions depending on the accuracy requirements in engineering monitoring tasks.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".