Using High-Rate GPS Data to Monitor the Dynamic Behavior of a Cable-Stayed Bridge
Bibliographic record
Abstract
This paper presents the results of the first real application, over one cable-stayed bridge, of a research under improvement since 2000 and presents the results of the monitoring of dynamic behavior under traffic load of Hawkshaw Cable-stayed Bridge, in New Brunswick, Canada. This bridge has 2 lanes, with total length of 301.20 m and its main span of 217.32 m is supported by two towers of 36 m of height. Two sessions of 1-hour GPS data have been conducted on the 30th October 2003. For each session, 5 GPS receivers (NOVATEL OEM4- DL4 and TRIMBLE 5700) were used, observing at a data rate of 0.2 seconds; one triaxial accelerometer and one total station. These GPS data were processed and analyzed by using a different method (SCHAAL et al., 2001; 2002; LAROCCA, 2004) that is described below. This method is part of a research in which the main objective is to confirm that GPS can be used as a trustworthy tool for characterizing the dynamic behavior of large structures, such as bridges, footbridges, tall buildings and towers, undergoing dynamic loads. Data analyses of Hawkshaw Bridge trial provide results that confirmed the potentiality of the method. Results presented on this paper agree with international trends in the engineering community. These trends include dynamic testing, monitoring of long span bridges and the dissemination and practice of monitoring systems that provide reliable data analysis and interpretation (SUMITRO, 2001; FARRAR, et al, 1999). This permits that assumptions used in the bridge design are correctly verified by conventional instrumentation and GPS. It is important to mention that there is not previous information about the Hawkshaw Bridge dynamic behavior, according to New Brunswick Department of Transportation.
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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 | 0.002 | 0.002 |
| 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) | 0.001 | 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".