Load-Bearing Behavior of Steel Fiber-Reinforced Concrete for Precast Tunnel Lining Segments under Partial-Area Loading
Bibliographic record
Abstract
During the assembly stage of precast segmental tunnel linings, the segments are often subjected to impact and concentrated loads. Since plain concrete is a quasi-brittle material exhibiting low tensile strength and fracture toughness, concrete damages - in the form of cracking and spalling - are very likely to occur on the periphery of the segments. By the addition of steel fibers into the concrete matrix, the robustness and ductility of this quasi-brittle material can be significantly enhanced due to the crack-bridging effect of fibers. To simulate the segments subjected to concentrated loads in small-scale, partial-area loading tests were carried out on plain and fiber concrete prisms under laboratory conditions. The principal variables investigated in this paper were fiber reinforcement, area ratio and loading eccentricity. The effects of those variables on the load-bearing capacity, failure mode and crack pattern were analyzed and discussed. From the experimental results, it was found that the presence of steel fibers led to a remarkable increase of the load-bearing capacity of concrete and changed its failure mode from a brittle to a ductile one. Increasing the area ratio or loading eccentricity also had significant influence on the bearing strength and failure pattern.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".