Flexural behaviour of concrete beams reinforced with fiberglass geogrid
Bibliographic record
Abstract
In cold regions, especially within Canada, the degradation of non-structural concrete components in challenging environmental conditions has become a pressing issue. Traditional steel reinforcements are known to be susceptible to corrosion. With anticipated climate shifts causing variations in temperature, precipitation, and freeze-thaw cycles, there's an increasing need for more resilient reinforcement materials to deter premature cracking in non-structural concrete components. This study delves into the advantages of using low-ductility fibreglass geogrids as reinforcement layers to curb crack development and augment the flexural performance of plain concrete beams. Tests were carried out on nine concrete beams, each measuring 550×150×150 mm, with diverse reinforcement setups. The emphasis was on evaluating load-deflection characteristics, energy absorption capabilities, and modes of failure. Results suggest that lowductility fibreglass geogrid reinforcement markedly enhances the flexural strength of plain concrete, outperforming control beams. Additionally, fibreglass reinforcement showcases enhanced crack resistance and postcracking behaviour than plain beams. A Finite Element Analysis (FEA) was also executed using the Abaqus software, and its accuracy was confirmed through experimental data comparisons, yielding numerical figures for midspan deflection and peak load. This research furnishes pivotal insights into the prospective use of progressive reinforcement materials to combat environmental challenges in colder climates.
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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 | 0.001 | 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".