Durability design of HPC bridge decks with lightweight aggregate and admixtures
Bibliographic record
Abstract
Highway bridges and parking structures, subject to coupled effects of mechanical loads and corrosion, often show early signs of distress such as concrete cracking and rebar corrosion leading to reduced structural performance and shortened service life. One solution to this problem is to use low-shrinkage low-permeability high-performance concrete (HPC) for bridge decks exposed to de-icing salts and severe loading conditions. A new HPC was formulated to achieve low shrinkage and low permeability, high early-strength, and 28-day compressive strength over 60 MPa (8,700 psi). Its mechanical performance and durability were tested both in the lab and field under severe test conditions, including restrained shrinkage, cycling loading, freezing and thawing cycles, and application of de-icing salts. Models were developed and calibrated to predict structural performance and service life of concrete bridge decks under severe exposure conditions. Prediction models indicate that bridge decks designed with low-shrinkage HPC can achieve a service life up to 100 years. Compared to normal concrete decks, short-to-medium span bridge decks using low-shrinkage HPC could be built at a comparable initial construction cost, but at less than 35% of the life-cycle cost.
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 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 | 0.000 |
| 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".