Projecting the resiliency of nano-modified cementitious composites with hybrid BFP/PVA fibers in shear key joints
Bibliographic record
Abstract
Effective bond strength at the interface between conventional concrete (CC) and high-performance fiber-reinforced cementitious composites (HPFRCC) is vital for applications like shear key for bridge joints. This study investigated the suitability of HPFRCC incorporating various constituents (cement, slag, as well as nano-silica), with only basalt fiber pellets (macro-BFP) or hybrid fiber systems including macro-BFP and micro-polyvinyl alcohol (PVA) fibers. BFP is a new emerging class of basalt fiber strands protected by a polymer coat. Experimental results (slant shear, pure shear, rebar pull-out, etc.) were used to develop modeling [homogenization/finite element modeling (FEM)] components to evaluate the effect of HPFRCC mixture design parameters on the mode of failure and bonding capacity with CC and steel rebar. After verification, these model components were integrated in a full-scale shear key joint model to project the field performance of the developed HPFRCC. The results revealed that nano-silica had a significant effect at improving the HPFRCC bonding strength capacity with CC and steel reinforcement. Whilst increasing BFP dosage (4.5%) in the nano-modified composites resulted in reduction of the interfacial bonding with CC, it significantly improved the rebar interfacial bonding and ultimate shear key capacity of the joint. Comparatively, the inclusion of 1% micro-PVA fibers in the nano-modified composites comprising macro-BFP resulted in the highest increase in shear resistance force, interfacial bonding with precast CC and shear reinforcement dowels, and ductility which suggests their promising potential to be employed in shear key jointing applications.
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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) | 0.000 | 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) | 0.000 | 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".