100-Year service-life bridge decks using low-shrinkage high-performance concrete
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 possible solution 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 has been formulated to achieve low shrinkage and low permeability, high early-strength, and 28-day compressive strength of up to 70 MPa. Its mechanical performance and durability have been 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. Prediction models have been 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 service lives exceeding 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 40% of the life-cycle cost over a 100-year period.
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.
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) | 0.002 | 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 it