Reliability-Based Design for the Flexural Capacity of Fiber ReinforcedConcrete Slabs on Ground
Bibliographic record
Abstract
The contribution of fibers in enhancing the mechanical behavior and providing a post-crack residual capacity of the concrete sections have widely been investigated and design approaches of fiber reinforced concrete (FRC) are established.These design approaches are usually uncertain and associated with inherent variability and modeling errors in which should be accounted for when designing reliable structures.The addition of fibers has further increased the range of uncertainties resulting in inconsistent levels of reliability for FRC structures when compared with those established for traditional reinforced concrete structures.To this end, this paper conducts a reliability-based analysis of the ultimate limit state (ULS) of the fiber reinforced concrete slabs (FRCS) on ground under flexural loading.The ULS is formulated based on the procedure adopted in the ACI 360R regarding the calculation of the post-crack moment capacity of fiber reinforced concrete slabs (FRCS) on ground under flexural loading.To ensure that the design procedure provides acceptable reliability levels, experimental results collected from previous studies were used in the statistical calibration.Monte-Carlo simulation was adapted to generate an array of random variables knowing their statistical parameters and distributions.Reduction factors for the flexural strength of FRC slabs corresponding to the load factors specified in the design codes were calculated and certain values are proposed to achieve target reliability levels.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".