Predicting long-term tensile degradation of GFRP rebars embedded in concrete with a reconsidered environmental reduction factor CE
Bibliographic record
Abstract
Glass fiber reinforced polymer (GFRP) has been considered as an advanced material to replace conventional steel reinforcements in concrete structures to address the corrosion issue. The degradation of GFRP rebars, which may threaten both the durability and safety of infrastructures, is a major concern. To predict the 100-year strength retention of GFRP under various temperature and relative humidity conditions, the environmental reduction factor ( C E ) is applied in engineering. The conventional C E based on the assumption of logarithmic degradation model is commonly utilized during the degradation phase spanning from a few years to decades; however, it is not applicable to the initial and perpetual degradation phases. To address this issue, a novel environmental reduction factor ( C E ′ ) based on the exponential degradation model considering temperature and relative humidity is proposed in this study. Both C E and C E ′ are mathematically deduced from empirical degradation data and then evaluated in a case study involving GFRP-concrete samples soaked in water at 23, 40 or 60 °C for up to 12 months within the authors’ dataset. Experimental results show that the GFRP tensile strength degradation is closer to the exponential model, reaching a plateau (47.4%) after 12-month exposure to 60 °C water. Moreover, the tensile strength retention of GFRP rebars in Vancouver (10 °C), Shanghai (16 °C) and Houston (22 °C) is predicted considering various scenarios of relative humidities (0–90%). Further research indicates that C E ′ (0.65–0.78) exhibits a smaller value compared to C E (0.81) at a temperature of 22 °C and a relative humidity of 90% following a 100-year exposure period, thereby providing engineers with a more conservative design approach for GFRP in real-world scenarios. Nevertheless, this exponential degradation model requires a thorough consideration of severe degradation state during the extended aging period, which may not be applicable to GFRP structures exhibiting exceptional durability. • Reduction factor enhance GFRP strength prediction, surpassing conventional method. • Degradation trend through empirical analysis, providing insights for long-term integrity. • Revelation of relative humidity's significant influence on GFRP strength degradation. • Implications of reduction factor ensure the structural reliability of GFRP in harsh conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".