Structural health monitoring of the Gr ndals Bridge in Sweden: the behaviour of CFRP strengthening in cold temperature
Bibliographic record
Abstract
To obtain a better knowledge of existing structures behaviour monitoring can be used. The use of monitoring in bridge structures by the use of instruments to assess the integrity of structures is not new and there are reports from structures tested as early as in the 19th century according to ISIS Canada1 However, the term SHM (Structural Health Monitoring) is relatively new to civil engineering and the driving force to implement SHM comes from recognising the limitations of conventional visual inspections and evaluations using conservative codes of practice. The possibilities to monitor existing structures with help of the rapidly evolving Information Technology are to day carried out. The objective of SHM is to monitor the in-situ behaviour of a structure accurately and efficiently, to assess its performance under various service conditions, to detect damage or deterioration, and to determine the health or condition of the structure1. In Sweden strengthening and periodic monitoring of a large freivorbau bridge (pre-stresed concrete box girder bridge) has been carried out, the Gr&diaero;ndals Bridge. The bridge is located in Stockholm and is approximately 400 m in length with a free span of 120 m. It was opened to tram traffic in year 2000. Just after opening cracks were noticed in the webs, these cracks have then increased, the size of the largest cracks exceeded 0.5 mm, and at the end of year 2001 the bridge was temporarily strengthened. This was carried out with externally placed prestressed steel stays. The reason for cracking is quite clear but the responsibility is still debated. Nevertheless, it was evidently that the bridge needed to be strengthened. The strengthening methods used were CFRP plates in the Service Limit State (SLS) and prestressed dywidag stays in the Ultimate Limit State (ULS). The strengthening was carried out during year 2002. At the same time monitoring of the bridge commenced, using LVDT crack gauges as well as optical fibre sensors. This monitoring was carried out during the summer period. In addition to this a winter monitoring was carried out in the beginning of 2005. This paper presents the background to strengthening and a comparison between summer and winter monitoring where the strengthening behaviour between the two seasons is enlightened. The result from the monitoring is very interesting; it would have been preferable to strengthen the bridge during the winter.
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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 | 0.000 |
| 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".