Tensile behavior of fiber-reinforced cementitious matrix with ultra-high-performance hybrid fiber-reinforced concrete (UHP-FRCM) with enhanced crack width control, fracture energy, and ultimate strength
Bibliographic record
Abstract
Fiber-Reinforced Cementitious Matrix (FRCM) is an effective solution for strengthening deteriorated concrete structures, valued for its lightweight design, concrete compatibility, and ease of application without formwork for underlay strengthening systems. This study aims to characterize the tensile behavior of a newly developed FRCM system by maximizing the load-bearing capacity through full utilization of Polyparaphenylene Benzobisoxazole (PBO) fabric mesh, leveraging its synergy with an eco-friendly UHPC matrix reinforced with hybrid steel and Ultra-High Molecular Weight Polyethylene (UHMW-PE) fibers. Tensile tests conducted per AC 434 evaluated the tensile response of FRCM systems incorporating PBO mesh with a UHPFRC matrix, using various combinations of steel and UHMW-PE fibers. A commercial FRCM system served as the reference. Digital Image Correlation (DIC) analysis was employed to measure crack width evolution. The proposed UHP-FRCM system fundamentally altered the collapse mechanism, achieving the full tensile capacity of the PBO mesh without compromising ductility. Compared to the reference system, the UHP-FRCM system demonstrated significant enhancements, with a threefold increase in bulk fracture energy and a twofold increase in ultimate strength, all while maintaining ductility. The results indicate that the addition of short fibers enhances the bond between the fabric mesh and the matrix through a bridging effect, which helps reduce interfacial slippage. Moreover, the hybrid use of steel and UHMW-PE fibers reduced crack widths at the serviceability stage, keeping them below the 100 μm threshold required for water impermeability. The developed UHP-FRCM system shows remarkable potential for advancing the strengthening of concrete structures, offering improved ductility, strength, and impermeability, providing an effective means to extend the service life of reinforced concrete structures.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".