FINITE ELEMENT MODELING OF STEEL-CONCRETE COMPOSITE BEAMS STRENGTHENED WITH PRESTRESSED CFRP PLATE
Bibliographic record
Abstract
Results from finite element modeling (FEM) of large-scale steel-concrete composite beams strengthened in flexure with prestressed carbon fiber-reinforced polymer (CFRP) plate were validated with experimental results and presented in this paper. The effect of varying the level of prestressing as percentage of the ultimate tensile strength of the CFRP plate was investigated. Comparison was carried out in terms of overall load-deflection behavior, strain profile along the length of the CFRP plate, and strain distribution across the depth of the beam at mid-span section. Very good agreement was observed between the finite element (FE) and the experimental results. The validated FE models were used to perform a comprehensive parametric study to investigate the changes in the behavior through wider range of prestressing levels and then, determine the optimum prestressing level that maintain the unstrengthened beams' original ductility (or energy absorption). An iterative analytical model was also developed, validated with both the FE model and the experimental results, and showed good agreement. A parametric study was carried out to investigate the effect of changing the yield strength of the steel and the concrete compressive strength on the moment of resistance of the section and the strain in the CFRP plate at ultimate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".