Experimental Investigation and Nonlinear Finite Element Analysis of Corroded Reinforced Concrete Elements Retrofitted with Strain-Hardening Cementitious Composites (SHCCs) and Fiber-Reinforced Polymers (FRPs)
Bibliographic record
Abstract
Abstract Corrosion-induced deterioration of reinforced concrete (RC) structures remains a major concern in structural engineering, especially in coastal or chloride-rich environments. Finding quick, long lasting and cost-effective solutions is a key point of modern civil engineering. In the study presented herein we investigated the structural performance of corroded RC elements strengthened with the commercially available innovative retrofitting materials such as Strain-Hardening Cementitious Composites (SHCCs) and Fiber Reinforced Polymers (FRPs). RC specimens are subjected to accelerated corrosion via the impressed current technique, then retrofitted with SHCC overlays. Flexural tests were used to evaluate the retrofits in terms of load-bearing capacity, ductility, and failure mechanisms. Image-based deformation measurements complement these tests, providing data for the validation of advanced nonlinear finite element models developed in ABAQUS. Numerical evaluation supported the experimental results. Additionally, a second remediating methodology Fiber-Reinforced Polymers (FRPs) was evaluated through FEA. Retrofitting corroded beams with SHCC markedly improved strength and ductility, restoring and exceeding uncorroded capacity by ∼55% in finite element analysis (FEA) whereas FRP retrofitting increased capacity by ∼67.6% compared to corroded beams, offering higher peak strength than SHCC but with more brittle post-peak behavior. SHCC proved highly effective for restoring strength and deformability, while FRP offered similar strength gains with less ductility. The experimental evaluation of FRP subjected specimens and the final comparison of the two methods is ongoing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".