Bonding Evaluation of Nanosilica-Modified Slag-Based Composites Comprising of Basalt Pellets and Polyvinyl Alcohol Fibers for Shear Joints
Bibliographic record
Abstract
The interfacial bonding between precast conventional concrete (CC) segments and cast-in place high-performance fiber-reinforced cementitious composites (HPFRCC) has received significant attention for jointing applications such as shear key fillers in bridge connections. Poor bonding of materials in these connections may lead to cracking, premature deterioration, and loss of monolithic behavior among segments. Among the emerging composites, cementitious composites comprising a multiscale reactive powder (nanosilica, slag, and cement) and strengthened with single and hybrid fiber systems of basalt fiber pellets (macro-BFP) and polyvinyl alcohol (micro-PVA) were prepared for potential use in shear transfer joints. BFP is a novel class of basalt fibers, where a polymeric resin of textured surface microgrooves was utilized to encapsulate the basalt strands. The study focused on the synergetic evaluation of the mechanical (compressive strength and tensile strength) performance of HPFRCC, and its bonding capacity (slant shear, bishear, and rebar pull-out) with CC and steel rebars. The mechanical trends were corroborated using thermal, microscopy, and mercury intrusion porosimetry studies. The results indicated that nanosilica enhanced the mechanical performance and interfacial bonding with CC and steel rebars. Reduction in mechanical strength and interfacial bonding were observed for specimens comprising a higher dosage (4.5%) of BFP (single fiber system) up to 21%; however, the tensile strain-hardening behavior and rebar interfacial bonding capacity were significantly improved up to 394%, and 174%, respectively. Comparatively, 1% micro-PVA fibers with 2.5% or 4.5% macro-BFP (hybrid systems) in the nanomodified composites resulted in marked improvement in terms of their mechanical properties (up to 47%) and interfacial bonding capacity with CC (up to 82%) and steel rebar (up to 29%), which suggests their promising use as fillers for shear key bridge joints.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 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".