Damage Assessment of a continuous Hollow Core Deck Bridge subjected to ASR
Bibliographic record
Abstract
An existing highway overpass located on a major motorway in Europe is examined on account of extensive longitudinal cracking on the lower face and sides of the deck, and signs of sustained damage in the piers. Material analysis reports have validated the existence of ASR activity in fine aggregates. The deck comprises a well reinforced hollow-core prestressed system, however longitudinal cracks penetrate to the interior of the hollow cores. The extent of damage is heavy considering that the laboratory values for free ASR expansion are below the threshold limits, suggesting that there may be underlying structural causes related to the response of the deck under traffic. Objective of the study is to interpret the reported damage, reproduce computationally the mechanics that led to the observed crack pattern and assess the residual structural capacity of the bridge. Detailed nonlinear finite element analysis is conducted to evaluate the structure and study the synergistic effects of structural demands, along with time-dependent phenomena and chemically induced expansion. The paper presents the numerical modeling and mechanistic evaluation of the findings through sensitivity analysis of various scenarios considered to reproduce the state of damage and to assess the effectiveness of various retrofitting strategies considered for bridge rehabilitation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".