Analytical and Experimental Study on Upgrading the Seismic Performance of Reinforced Masonry Columns Using GFRP and CFRP Wraps
Bibliographic record
Abstract
In the last decade, extensive research has been done in the field of the seismic upgrading of reinforced concrete (RC) columns using fiber-reinforced polymer (FRP). The enhancement in the column’s ultimate strength and significant improvement in the column’s displacement ductility combined with the ease of FRP installation resulted in an increasing use of FRP as an effective retrofit solution. Recently, similar enhancements to the RC columns have been reported for reinforced masonry columns (RMCs) upgraded using FRP. However, due to the limited available studies, a gap still exists in understanding the lateral response of the upgraded RMCs using different types of FRP. This study presents an experimental investigation of the effect of changing the FRP type (carbon and glass) on the lateral response of RMCs tested under quasistatic cyclic loading. Based on the experimental data, both FRP materials showed higher strength and ductility compared with control specimens not FRP-wrapped. However, the carbon FRP (CFRP) upgraded RMCs showed slightly higher strength and higher ductility levels than that of glass FRP (GFRP). Also, the difference between carbon and glass FRP RMCs performance was more noticeable as the confinement ratio increased. Moreover, with the current migration of the design codes from forced-based to displacement-based design, there is a need to have simple analytical tools capable of predicting the full load-displacement relationship. Therefore, 12 masonry prisms having various numbers of FRP layers and configurations were tested under concentric compression loading to calibrate the stress-strain material model. Consequently, this stress-strain model was implemented in a simple backbone model capable of predicting the lateral load-displacement backbone relationship of the upgraded RMCs. The model was capable of calculating the ultimate strength, displacement at ultimate strength, and initial stiffness of the tested RMCs with average errors of 6, 22, and 30%, respectively.
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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 | 0.000 |
| 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) | 0.000 | 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".