Reliability analysis of residual service life of restored concrete bridge
Bibliographic record
Abstract
The Wan Jiang Bridge in China was damaged by ship impact and subsequently restored. This paper presents a detailed evaluation of the residual service life (RSL) of the bridge using the reliability analysis method of the Joint Committee on Structural Safety, based on the structural load-bearing capacity (LBC) of the bridge and the design load. Data were collected from bridge design codes, field loading tests and finite-element (FE) analysis. The LBC was initially calculated and subsequently revised using specific coefficients. Using FE analysis, the bending moment on the control section of the bridge was determined under the most unfavourable loading conditions and the ultimate LBC of the bridge was checked. Subsequently, static and dynamic loading tests on the restored bridge were conducted, with the load and capacity determined for the control section based on FE analysis. The loads considered were dead load, vehicle load and crowd load. A time-dependent reliability index was developed for the restored bridge using the probability distributions of capacity and design load variables, and the RSL was determined. This case study, predicting the RSL of the bridge based on a multitude of data, should be valuable for future bridge maintenance and management.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.000 | 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".