GFRP-Retrofitted Reinforced Concrete Columns Subjected to Simulated Blast Loading
Bibliographic record
Abstract
This paper presents experimental results of one as-built and three glass-fiber-reinforced polymer (GFRP)-retrofitted reinforced concrete columns subjected to simulated blast loading. Retrofitting involved various configurations of longitudinal and transverse GFRP layers to enhance flexural and shear capacity. The objective was to study the performance and level of protection of the retrofitted columns to mitigate blast effects. The results demonstrated that retrofitting can significantly increase the strength and stiffness of reinforced concrete flexural members and greatly improve blast response. Furthermore, the addition of transverse GFRP wraps can lead to enhancements in the debonding strain and behavior of longitudinal GFRP, as well as an increase in postpeak ductility of concrete. A complementary analytical study based on the single-degree-of-freedom (SDOF) dynamic analysis method was conducted to predict the displacement response of the columns. The load–deformation relationships of the columns were computed using a lumped inelasticity analytical model. In addition, modifications to a standard degrading stiffness hysteretic model were proposed to account for accumulated damage due to repeated testing. Satisfactory agreement between the SDOF-predicted and experimentally recorded maximum displacements, time to maximum displacements, and residual displacements were obtained.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".