Design code development for fibre‐reinforced polymer structures and repairs
Bibliographic record
Abstract
Abstract This paper presents the issues and solutions in developing design codes and standards for the use of fibre‐reinforced polymer (FRP)‐reinforced concrete structures. First, the codes for new construction, referring mainly to the Japan Society of Civil Engineers (JSCE) Recommendations for Design and Construction of Concrete Structures using CFRM (Continuous Fibre Reinforcing Materials), the Canadian Highway Bridge Design Code (CHBDC), and the American Concrete Institute (ACI) Committee 440 recommendations are outlined. Methods discussed are: structural analysis; determination of design values; examination of flexural and shear capacity; precautions to ensure ductility or deformability; and calculations of deformation and development length. Similarly, design codes developed for strengthening of concrete structures with FRP, namely the recommendations by the JSCE, ACI Committee 440, and the fib code are compared and discussed with respect to the ways in which FRP may contribute to improvement of the performance of structures in flexure, shear and ductility. Test methods for different material properties of FRP and the bond of FRP to concrete are also presented, with reference to the JSCE recommendations.
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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".