Experimental assessment of post-processed kinematic Precise Point Positioning method for structural health monitoring
Bibliographic record
Abstract
Monitoring the response of engineering structures, such as tall buildings, tower and large-scale bridges, under severe loading conditions, such as strong earthquake or wind storm, is an important requirement to verify their design and construction and to evaluate structural condition and reliability. In the last two decades, high-rate real-time or post-processed kinematic differential Global Positioning System (DGPS) has been widely used in dynamic displacement measurements of civil engineering structures. In recent years, interest has increased for Precise Point Positioning (PPP) due to its capability to generate positioning solutions as accurate as DGPS. In this study, the potential of post-processed kinematic PPP in terms of monitoring dynamic displacement response of a structure has been explored based on free damped oscillation events obtained from a model structure, which is able to vibrate in the fundamental and higher modes of vibration. A number of experiments have been carried out and five events, each of which is different character, have been selected to compare PPP results with DPGS results in the time and frequency domain. The results clearly demonstrate that the PPP method, like the DGPS method, offers great potential for the measurement of horizontal and vertical dynamic movement of structures. The impact of a short period (one minute) of observation length on the result of the kinematic PPP method was also investigated in terms of sensing the dynamic movement of a structure. Twenty selected one-minute data-sets extracted from a one-hour original data-set were processed by Canadian spatial reference system PPP and each one-minute PPP solution was compared with the corresponding segment obtained from the one-hour PPP solution. The results show that the one-minute PPP solution is able to extract the fundamental natural frequency of the oscillation in the horizontal and vertical component just like the one-hour PPP solution after the offset is removed and the lower frequency trend component is filtered out.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".