Mechanical and Durability Characteristics of Latex‐Modified Fiber‐Reinforced Segment Concrete as a Function of Microsilica Content
Bibliographic record
Abstract
This study evaluated the performance of latex‐modified fiber‐reinforced concrete (RC) segments as a function of the substitution level of microsilica and type of reinforced fiber, to address the problem of corrosion of steel segments and steel‐reinforced fiber segments, which are commonly used to shield tunnel‐boring machine (TBM) tunnels in urban spaces. Our study compared macro synthetic, steel, and hybrid (macro synthetic fiber + polypropylene fiber) reinforcing fibers. The substitution levels of microsilica used were 0, 2, 4, and 6%. The target strengths were set at 40 and 60 MPa to test compressive strength, flexural strength, chloride ion penetration resistance, and impact resistance. Testing of latex‐modified and fiber‐reinforced segment concrete showed that the compressive strength, flexural strength, and chloride ion penetration resistance increased with an increasing substitution level of microsilica. These improvements were attributed to the densification of the concrete due to filling micropores with microsilica. Micro synthetic fiber was more effective in terms of improved compressive strength, flexural strength, and chloride ion penetration resistance than steel fiber. These results were due to the higher number of micro synthetic fibers per unit volume compared with steel fiber, which reduced the void volume and suppressed the development of internal cracks. The optimal microsilica content and fiber volume fraction of micro synthetic fiber were 6% and 1%, respectively. To evaluate the effects of the selected mixtures and hybrid fibers simultaneously, other mixing variables were fixed and a hybrid fiber mixture (combination of macro synthetic fibers and polypropylene fibers) was used. The hybrid fiber mixture produced better compressive strength, flexural strength, chloride ion penetration resistance, and impact resistance than the micro synthetic fibers.
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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) | 0.001 | 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".