Sustainable seismic retrofitting of masonry walls using FRP composites: numerical analysis and parametric optimization
Bibliographic record
Abstract
Abstract Masonry structures, despite their widespread historical use, inherently exhibit high brittleness, which renders them prone to cracking and structural failure under various loading conditions; to deepen the understanding of masonry performance and offer valuable insights for the design and retrofitting of such structures, this study employs Finite Element Analysis (FEA) to investigate the structural response of masonry walls under two types of loading—cyclic concentrated transverse point loading and monotonic loading. To mitigate in-plane cracks, three strengthening schemes using Fiber Reinforced Polymer (FRP) sheets were implemented and their effectiveness compared, with results showing that FRP sheets achieved superior crack control performance; furthermore, a parametric study was carried out to evaluate different FRP sheet configurations under cyclic loading, focusing on their effects on force-displacement behavior, crack morphology, and peak load capacity. Among the tested configurations, the FRP layout of Case 1 (diagonal configuration) notably improved seismic resistance by reducing sliding failure and achieving more efficient stress distribution—resulting in an approximately 62% increase in peak force compared to the control model—and the results further highlight that Case 1 exhibits superior energy dissipation capacity, ductility, and stiffness retention, making it the most effective strengthening scheme for enhancing the seismic resilience of masonry walls. The findings of this study are anticipated to play a critical role in optimizing masonry retrofitting strategies, thereby facilitating the development of resilient and structurally efficient masonry walls for seismically active regions.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".