The Investigation of the Shear Capacity of Fiber Reinforced Polymer (FRP) Deep Beams.
Bibliographic record
Abstract
The use of fiber-reinforced polymer (FRP) reinforcement in deep beams is expected to grow due to its ability to address corrosion issues in concrete structures. FRP bars offer advantages such as high strength-to-weight ratio, fatigue resistance, and non-corrosive properties. These bars are emerging as a viable alternative to traditional steel reinforcement. Certain structures like underground tunnels, transfer girders, and bridges often require large concrete sections with low shear span-to-depth ratios (a/d), which generally transfer load through arch action. However, there is no simple method to estimate the shear strength of such members, and current strut-and-tie models are complex and yield varying results. A simplified approach to estimate shear capacity is needed for engineers. The shear span-to-depth ratio (a/d) significantly impacts the shear strength of FRP-reinforced members. Based on research, modifications to design codes, particularly for deep beams (a/d <2.5) and FRP-reinforced concrete members, have been proposed by the American Concrete Institute (ACI) and Canadian Standards Association (CSA). This paper investigates the shear capacities of two deep beams, one simply supported and reinforced with carbon fiber-reinforced polymer (CFRP) and the other continuous and reinforced with glass fiber-reinforced polymer (GFRP) bars. Both were analyzed using ABAQUS/CAE 2017 software, and a sensitivity study was conducted comparing results with experimental data from existing studies. The shear span-to-depth ratio varied from 1.0 to 2.5. The results indicated that increasing the ratio significantly reduced load capacity, and continuous beams showed higher load capacity than simply supported beams.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".