Cyclic performance of reinforced concrete T-beams strengthened in shear with fiber-reinforced polymer composites: Sheets versus laminates
Bibliographic record
Abstract
Much existing reinforced concrete (RC) civil infrastructure worldwide is in need of shear strengthening and rehabilitation. The use of externally bonded (EB) carbon fiber-reinforced polymer (CFRP) sheets and laminates to strengthen deficient RC beams in shear is now an acceptable and cost-effective practice, particularly under static loads. However, due to its complexity, the cyclic (fatigue) behavior and performance of shear-strengthened beams is not fully documented. Recently, use of CFRP continuous sheets wrapped over the shear length for fatigue upgrade has been studied. This technique may present drawbacks related to surface preparation and FRP debonding. Therefore, when possible, prefabricated CFRP L-shaped laminates can be a cost-effective alternative because they require less surface preparation and do not peel off easily. The objective of this paper was to present the results of an experimental investigation that compared cyclic (fatigue) and static (post-fatigue) behavior of two EB CFRP techniques (sheets vs. laminates) for shear retrofit of RC T-beams. In total, six laboratory tests on full-size 4520-mm-long beams were conducted. The specimens were subjected to fatigue loading of up to 6 million load cycles at a rate of 3 Hz. Specimens that sustained 6 million cycles were then tested monotonically up to failure. The variables examined in the paper were: (1) the EB CFRP strengthening scheme, and (2) the presence and ratio (spacing) of internal shear reinforcement. The test results confirmed the effectiveness of using EB CFRP shear-strengthening methods under cyclic loading. They also revealed that the fatigue performance of RC T-beams strengthened with L-shaped laminates is significantly superior in extending fatigue life compared to corresponding T-beams strengthened with U-wrapped sheets. This was quantified in terms of deflection response (47% increase due to fatigue loading for specimens with L-shaped laminates compared to 90% for specimens with U-wrapped sheets), and the level of increase in steel-stirrups strain range (9–42% vs. 62–114%) and in EB CFRP (36–97% vs. 58–163%).
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 | 0.000 | 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".