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Record W4404733224 · doi:10.1016/j.dibe.2024.100583

Predicting long-term tensile degradation of GFRP rebars embedded in concrete with a reconsidered environmental reduction factor CE

2024· article· lv· W4404733224 on OpenAlex
Peng Wang, Yajie Zhou, Yao Lu, Linyuwen Ke, Haoliang Wu, Weiwen Li, Christopher K.Y. Leung

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueDevelopments in the Built Environment · 2024
Typearticle
Languagelv
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsnot available
FundersGuangdong Science and Technology DepartmentResearch Grants Council, University Grants CommitteeShenzhen UniversityNational Natural Science Foundation of ChinaNatural Science Foundation of Shenzhen City
KeywordsFibre-reinforced plasticUltimate tensile strengthDegradation (telecommunications)Term (time)Reduction (mathematics)Materials scienceStructural engineeringForensic engineeringComposite materialEngineeringMathematics

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.296
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.020
GPT teacher head0.227
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it