Thermal-mechanical numerical investigation of glass fibre-reinforced polymer-reinforced concrete beams with mid-span straight-end bar lap splices subjected to standard fire
Bibliographic record
Abstract
Purpose Glass fibre-reinforced polymer (GFRP) has several advantages, including durability and corrosion resistance. However, there is some skepticism regarding the overall strength of GFRP-reinforced concrete elements when subjected to elevated temperatures, particularly regarding the degradation of bond strength between GFRP bars and concrete. Design/methodology/approach Four full-size GFRP-reinforced concrete beams with straight-end bar lap splices at their midspan, which were examined in two prior related experimental studies under standard fire exposure, have been modelled using the ABAQUS software. A thermal-mechanical numerical analysis was performed to study the performance of the beams under four-point bending in simulated standard fire conditions. In a subsequent stage, a parametric study was conducted to investigate the effects of the applied load ratio and the number of reinforcing GFRP bars. Findings Results show that increasing the load ratio slightly increased the beam mid-span deflections throughout most of the simulated duration of standard fire exposure. However, under higher load ratios (80%–100%), a significant increase in the beam mid-span deflection was observed immediately before failure, when the surface temperature of the reinforcing bars approached their glass transition temperature. Based on the outcomes of this new numerical study, using fewer but larger-diameter GFRP bars is recommended over using more bars of smaller diameter with the same total cross-sectional area for enhanced fire resistance. Originality/value The outcomes of this research can assist researchers and practitioners during the design stage of GFRP-reinforced concrete beams to determine the optimum number and diameter of GFRP bars to enhance their fire resistance. Additionally, the outcomes can be highly beneficial when rehabilitating fire-damaged concrete beams, as the maximum safe load that can be applied to GFRP-reinforced concrete beams in the event of a fire can be accurately determined.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".