Anchorage and lap capacity of plain surface and square twisted bars in existing R.C. Structures: A comprehensive approach
Bibliographic record
Abstract
Abstract Although plain round or square section reinforcement is no longer used in new construction, there are many older structures still in service built with such bars. In recognition of the need for guidance on the assessment of load‐carrying capacity of structures reinforced with such bars, design codes are now introducing or re‐introducing provisions to assess the capacity of laps and anchorages of such reinforcement. The authors have contributed to the introduction of design rules in the fib Model Code 2020. This paper describes the derivation of these provisions. Starting from the form of expression proposed earlier by the authors, the data from which these proposals are derived is specified and modifications in interpretation preparatory to a statistical analysis are outlined. A rigorous multivariate statistical procedure is then employed firstly to determine a mean strength expression for the capacity of anchorages and laps which is then validated against test data. Further statistical analysis is subsequently used to determine a design expression using the approach outlined in EN 1990, taking appropriate account scatter in test data. Finally, some comparisons are presented between the assessment expressions proposed and design provisions that were in place when these types of bars were in common use. Areas where earlier design provisions may be non‐conservative are identified.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".