Improving methods of strength design of normal sections of flexural concrete members reinforced with fiber-reinforced polymer bars
Bibliographic record
Abstract
Abstract Theoretical and experimental studies of the strength of normal sections of flexural concrete members reinforced with FRP bars have been conducted. The test specimens were concrete beams 1810 mm long, with a rectangular section of 120×220 mm, reinforced with two bars in the tensile area. The beams were reinforced longitudinally with steel, glass fiber-reinforced polymer (GFRP) and basalt fiber-reinforced polymer (BFRP) bars. The design methods of guidelines of Russia, the USA, Canada and the European Union have been considered. There are two approaches to the strength design of normal sections – the European and the North American. The approach used in the design method of the Russian guideline SP 295.1325800.2017, when all partial safety factors are used in calculating the design characteristics of materials, causes an overestimation of the boundary values of relative depth of the compressed region ξR . It leads to an inaccurate determination of the failure mode and possible over-reinforcement of the construction. Some corrections have been brought about to the design methods of the Russian guideline SP 295.1325800.2017. Factor β has been introduced, which takes into account prestressing of the reinforcement. As a result, the deviation of the theoretical evidence from the experimental values of failure moments decreased from 30.44 % to 13.2 %. Changes have been made to the approach for the application of safety factors, which allowed increasing the accuracy of determining the failure mode and bring the safety factor for members C to the value of 1.6 adopted by the authors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".