Experimental and Analytical Study on Precast High-Strength Concrete Tunnel Lining Segments Reinforced with GFRP Bars
Bibliographic record
Abstract
Replacing steel reinforcement with glass fiber-reinforced polymer (GFRP) reinforcement is an effective solution to avoid the corrosion problem in precast concrete tunnel-lining (PCTL) segments. In addition, using high-strength concrete (HSC) can improve the durability of concrete in the harsh environment of tunnels. This study pioneers in investigating the structural performance of GFRP-reinforced PCTL segments constructed with HSC by testing four full-scale specimens measuring 3,100 mm in length, 1,500 mm in width, and 250 mm in thickness under a three-point bending load. The investigated parameters included concrete compressive strength [normal-strength concrete (NSC) and HSC], reinforcement ratio (0.48% and 0.90%), and tie configuration (closed ties with U-shaped ties). The results are presented and discussed in terms of cracking behavior, failure mechanism, deflection behavior, strain in reinforcement and concrete, ductility, and deformability. An analytical investigation was carried out to evaluate and modify the existing design provisions (ACI 440.1R-15, CAN/CSA S806-12, CAN/CSA S6-19, and AASHTO 2018) for use in predicting the shear and flexural strength of GFRP-reinforced HSC PCTL segments. The results indicate that using HSC improves the flexural and shear strength of GFRP-reinforced PCTL segments, while it has a minimal effect on the postcracking stiffness and cracking behavior of the specimens. According to the analytical investigation, the procedure presented to modify ACI 440.1R-15 can be used to predict the flexural capacity of GFRP-reinforced HSC PCTL segments with high accuracy. In addition, CAN/CSA S806-12 predicts the shear capacity of HSC-GFRP-reinforced PCTL segments with an error of less than 7.0%.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".