Load Identification on an Arch Bridge Based on the Generalized Flexibility Matrix
Bibliographic record
Abstract
Health problems of the bridge structure are a hot issue in engineering research currently. Load identification together with damage identification is an important method to diagnose the health status of the bridge. In order to identify different loads on bridges, this paper takes a box arch bridge built with prestressed reinforced concrete in Chongqing as an example and establishes it’s finite element model. The general flexibility matrix is obtained by calculating the response values of each control section separately caused by each unit characteristic load. The maximum bending moment of each control section is acquired by using the time history analysis of Wenchuan Seismic wave, and the equivalent loads are derived from the general flexibility matrix. Results indicate that this method can identify a wide range of basic load types and accurately identify the actual load of the bridge with high precision. Thus the actual operating conditions of bridge can be better reflected and the bridge’s safety condition can be evaluated. Besides, the load evaluation and maintenance of the bridge can be based on this.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".