Rehabilitation of reinforced concrete beams subjected to torsional load using ferrocement
Bibliographic record
Abstract
Recent exploration has focused on repairing and strengthen reinforced concrete (RC) beams subjected to torsional loading through various techniques. However, limited attention has been given to enhancing RC beams against torsional loading using ferrocement, resulting in inadequate evidence to illustrate its benefits. Torsional failure, a brittle and undesirable type of failure, is especially problematic in earthquake-prone regions. This paper presents an experimental study that evaluates the behavior of RC beams repaired with ferrocement when subjected to pure torsion. The study included testing six RC beams with identical cross-sectional dimensions (150 × 350 mm) and a total span of 1400 mm. The beams were divided into three segments, with a 1000 mm experimental region and two cantilever sections (200 mm wide and 350 mm long) that were extensively reinforced to prevent early failure during testing. Two primary factors were investigated: the effect of ferrocement configuration and the number of layers in the ferrocement reinforcing mesh. The tested beams provided insights into cracking, ultimate torques, rotation angles, toughness, and failure patterns. Experimental results revealed that the proposed ferrocement repair techniques unevenly enhanced the ultimate load-carrying capacity of the repaired beams, excluding U-jacket wrapping. Particularly effective was full wrapping with dual-layer wire mesh, increasing capacity by 28% compared to control beams. Using strip wrapping ferrocement with two mesh layers improved load-carrying capacity by 10% compared to control beams, while strip wrapping with one layer increased capacity by 6%. Ultimately, increasing the number of layers in the ferrocement reinforcement mesh led to an escalation in ultimate torsional capacity. This study confirms the potential of ferrocement as a strengthening method and emphasizes the significance of optimal repair configurations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".