Measure point arrangement strategy for in-service continuous girder bridge SHM with consideration of structural robustness (Special Issue of ICAST 2019)
Bibliographic record
Abstract
In-service bridges, under long-term service operational environment, are usually accompanied by different damage types. Traditional method for the measure point arrangement of in-service bridge SHM is usually based on engineering experience. A large number of SHM sensors are usually arranged on the structure, followed by a high engineering cost and a heavy maintenance task. These sensors will also produce large amounts of data, creating a challenge for operators requiring to deal with data processing in an effective manner. This study serves as a part of the series of studies on the measure point arrangement strategy of in-service bridge SHM. In this study, the SHM sensor measure point arrangement of in-service continuous girder bridge (a common structural style of high way bridge in China) is proposed. Two-span continuous beam, three-span continuous beam, and four-span continuous beam are taken as examples. Detailed comparison and verification are also performed with consideration of numerical simulation and previous studies. Different traffic speeds and different bridge spans are considered. The effect of different damage locations and different damage degrees are analyzed in detail. This study shows that a general similar trend can be observed for the structural robustness of in-service continuous girder bridge. The elements with smaller structural robustness of this kind of bridge are basically located around the middle cross section of side spans (first span and last span), followed by the middle span. Moreover, the numerical value of structural robustness of different elements in a continuous girder bridge is significantly different from each other, due to the complexity of the joint effect of different traffic speeds and damage locations. Therefore, the measure point should be generally arranged at the side span firstly, followed by the middle span. With consideration of the specific traffic speed and damage location in engineering application, a detailed analysis is also proposed for the further optimization of SHM sensor measure point arrangement. Once the elements are arranged in order of the numerical value of structural robustness, the SHM sensor measure point arrangement of this kind of bridge can be more targeted, and the number of sensors can also be greatly reduced.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".