FRP for Sustainable, Resilient, and Seismically Resistant Concrete Structures
Bibliographic record
Abstract
The research, underway at the University of Toronto, investigates FRP-reinforced concrete structures under extreme environments and extreme loads such as seismic to establish robust design guidelines.This article summarizes the performance of FRP and FRP-reinforced concrete structures under extreme weather conditions, addressing durability concerns exacerbated by climate change.FRP composites, including bars and sheets, offer corrosion resistance, lightweight properties, and high strength-to-weight ratios, making them viable alternatives to steel reinforcement.However, their susceptibility to degradation under extreme temperatures necessitated rigorous evaluation.A multi-phase experimental and analytical research program was conducted, encompassing tensile tests, bond assessments, beam analyses, and FRP-wrapped cylinder studies under varied thermal and environmental conditions.Test results revealed critical insights: FRP sheets exhibited a 30-40% bond strength reduction at 60°C (exceeding resin glass transition temperatures), while GFRP bars experienced up to 26% bond strength loss after prolonged 80°C exposure.Over-reinforced beams, aligned with Canadian design codes (CSA S806-12/S6-19), demonstrated minimal strength degradation (≤5%) under thermal conditioning, underscoring the importance of design philosophy.Externally FRP-wrapped shear-critical beams showed enhanced strength (up to 112% for GFRP and 96% for CFRP under ambient conditions), though elevated temperatures reduced effectiveness by 10-20%.FRP confinement improved concrete cylinder ductility by 600%-700%, yet epoxy softening at 60°C diminished strength gains by 15% to 53%.Freeze-thaw cycles had a negligible impact on FRP-wrapped specimens.Analytical efforts yielded empirical models predicting GFRP bar tensile strength decay at elevated temperatures and theoretical models for the shear capacity of FRP-reinforced beams, validated with experimental data (average predicted-to-test ratios of 0.96-1.00).The findings summarized in this article advocate for revised code provisions to account for temperature-dependent FRP performance, ensuring sustainable, resilient infrastructure.By integrating material behaviour, structural response, and environmental effects, this research advances FRP applications in climate-adaptive construction.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".