Behavior of post tensioned concrete slabs with various strenghening techniques
Bibliographic record
Abstract
Abstract Strengthening post‐tensioned (PT) slabs is essential for enhancing their load‐carrying capacity and overall structural performance in a wide range of practical applications. This study investigates the effectiveness of two strengthening techniques—external reinforced concrete (RC) jacketing and carbon fiber‐reinforced polymer (CFRP) reinforcement—through both experimental testing and numerical modeling. Five one‐way PT slabs were fabricated and tested under four‐point bending, comprising one control specimen and two specimens for each strengthening method. The effects of varying the reinforcement mesh configuration in the RC jacket and the number of CFRP layers were examined. Key performance indicators, including crack patterns, midspan deflection, and ultimate load capacity, were recorded during testing. Experimental failure loads were compared with predictions from international design codes. The results indicated that PT slabs strengthened with RC jacketing exhibited load capacities exceeding those predicted by AS 3600, ACI 318, and Eurocode 2 by 2.7%–19.7%, while CFRP‐strengthened slabs showed improvements ranging from 6.9% to 12.9%. A robust finite element model (FEM) was developed and validated against the experimental data, incorporating the nonlinear behavior of concrete, steel reinforcement, prestressing tendons, and CFRP. A comprehensive parametric study, involving 54 numerical simulations, was conducted to evaluate the influence of critical parameters on the load capacity of strengthened slabs. The results demonstrated that increasing the yield strength and diameter of the jacketing reinforcement, as well as the compressive strength of the jacketing concrete, significantly enhanced load capacity. Furthermore, increasing the number of CFRP strips improved the performance of strengthened slabs by 3.4%–18.9%.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".