SAP2000 Analysis of Seismic Reinforcement Using Carbon Fiber Reinforced Polymer and Textile Reinforced Mortar Jacketing
Bibliographic record
Abstract
Many global structures, not initially designed to endure seismic forces, necessitate seismic strengthening.Existing research has explored diverse strengthening methodologies and assessed the seismic performance of these solutions.In the present study, a comparative analysis of a structure's seismic behavior before and after strengthening the building's columns was conducted using nonlinear time-history analysis.Two reinforcing techniques, Textile Reinforced Mortar (TRM) and Carbon Fiber Reinforced Polymer (CFRP) jacketing, were applied to a reinforced concrete building.Both techniques aimed to achieve full composite action (full bond) between the jacketing material and the existing concrete columns.The SAP2000 software was employed, strictly adhering to the Federal Emergency Management Agency (FEMA) 356 specifications for beams and column hinges.The analysis treated beam and column elements as nonlinear frame components, defining plastic hinges at their respective ends to simulate their behavior.Specifically, the beams were modeled to only possess moment (M3) fiber hinges, while columns were designed to accommodate axial load and biaxial moment (PMM) fiber hinges.The numerical findings indicated an average increase of 8% and 5% in base shear for buildings strengthened with CFRP and TRM jackets, respectively, when compared to non-strengthened buildings.Maximum story displacement increased by 9% and 4% correspondingly when the building was enhanced with CFRP and TRM jackets, compared to the original structure.It is noteworthy that both methods required a similar number of columns to be strengthened, yet the total cost of CFRP was found to be approximately 35% higher than that of TRM.
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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 | 0.000 | 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) | 0.001 | 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".