Bibliographic record
Abstract
Despite a recent high-profile application at Hammersmith Flyover, adoption of Ultra High Performance Concrete (UHPC) in the UK remains limited. This contrasts with use globally which continues to grow. A state of the art study was undertaken on behalf of Highways England, the government-owned company responsible for operating and maintaining the UK’s Strategic Road Network, with objectives to identify possible applications and benefits of UHPC, barriers to use,and recommendations to promote increased use on Highways England’s infrastructure. Applications for new-build (full components, in-situ connections) and structural enhancement(link slabs, deck overlays, column jacketing) were identified. Highways England owns a wide range of transport infrastructure, including 6800+ concrete bridges, the majority of which are over40 years old. This makes structural rehabilitation an important topic, and provides justification for promoting implementation of UHPC. The absence of UK standards and guidance for design and execution of UHPC is a key barrier to widespread adoption; some other European countries making use of UHPC have some form of published literature. Further issues include lack of experience amongst designers and contractors, limited numbers of UHPC suppliers, and the absence of knowledge and precedent regarding technical approval. Recommendations are made for stimulating use of UHPC in the short term and include preparation of an action plan identifying additional sources of funding (e.g. innovation funds), conducting a whole life cost benefits analysis, and developing a clear approvals process for UHPC. To pave the way for widespread implementation in the longer term, recommendations include sponsorship of pilot projects,engagement with the academic community, and promotion amongst designers and sub-contractors.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.242 | 0.062 |
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".