Concrete Shear Walls with GFRP Bars: Simulation and Economic Design
Bibliographic record
Abstract
Premature corrosion of reinforcing steel is a significant concern for steel-reinforced concrete (RC) structures, often leading to deterioration before reaching their design life. To address this issue, glass fibre-reinforced polymer (GFRP) bars have proven effective as a corrosion-resistant alternative in structural elements such as beams, columns, and slabs. Recent studies have shown that concrete shear walls reinforced with GFRP bars exhibit acceptable performance in terms of ultimate strength. However, compared to conventional steel reinforcement, limited data exist regarding their cracking, deformation, creep susceptibility, long-term performance, and cost. In this paper, a parametric study using a finite-element analysis model, validated against experimental data, was conducted to evaluate the effect of common design variables in GFRP-reinforced concrete shear walls. The study identified optimal design solutions where GFRP-reinforced walls outperform conventional RC walls. The analysis revealed that optimal GFRP designs cost approximately 1.5 times more than steel-reinforced walls, with deflection and crack width emerging as critical factors influencing design feasibility. The use of high-strength concrete was found to have minimal impact on the feasible design region, while bond strength between GFRP bars and concrete significantly influenced crack width and overall performance. Furthermore, creep rupture was determined not to be a critical concern under typical loading conditions. The results highlight that feasible GFRP designs are governed by service conditions, whereas ultimate strength remains the primary constraint for steel-reinforced walls.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".