Bibliographic record
Abstract
Global changes in temperature, precipitation, and wind patterns threaten the integrity and functionality of existing highway bridges.The expected upsurge in climate change can accelerate material and structural degradation and induce additional stresses that increase the risk of failure of critical components of existing bridges.As a result of climate change, growing rates of chloride ingress into concrete and rising rates of reinforcing steel corrosion are expected.Extreme levels and high variations of temperatures can seriously affect bridges' performance.The increase in climate loads and frequency of extreme weather events can impact the safety and serviceability of bridges and the recovery time after major storms.Infrastructure owners will decide the recovery plan and the required capacity after extreme weather events based on the bridge's importance to the transportation network.The recovery time is affected by the level of damage the bridge experiences, the required performance of the bridge after an extreme event, and the rehabilitation or reconstruction approach.Based on the bridge's significance and the required load capacity, different performance levels will be discussed as targets of the performance recovery plan of the most popular bridges,.The recovery speed presents the effectiveness of the bridge's resilience.Key cost-effective resiliency-enhancement approaches will be presented; for instance, accelerated bridge construction would provide the highest possible recovery time versus the classical in-site construction approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 teacher head, 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".