Bibliographic record
Abstract
This article describes a promising new material called Ductal for use in bridges. Ductal is an ultra-high performance, fiber-reinforced cement composite material. It offers superior technical characteristics (including ductility, strength and durability) while providing highly moldable products with a high-quality surface aspect. This unique combination of properties allows designers to create thinner sections and longer spans that are lighter, more graceful and more innovative in geometry and form while providing improved durability and impermeability. Compressive strengths for bridge applications range up to 30,000 psi, with flexural strengths of up to 6,000 psi. The first industrial use of Ductal was a pedestrian bridge in 1997. Since then, several pedestrian bridges have been constructed successfully using Ductal. The Federal Highway Administration is currently investigating Ductal as a possible solution for the replacement of deteriorating highway bridges. One challenge facing highway bridge engineers is how to design bridges using this technology when current design codes and standards do not provide guidance. In response, several international groups have developed interim design guides. Optimized shapes and profiles for each use also need to be developed so that the industry can invest in the appropriate formworks to produce optimized pieces. Once these obstacles are overcome, the true merits of Ductal for bridge engineering can be fully recognized.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.013 |
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".