Comprehensive Evaluation of Geopolymer Concrete for Enhanced Railway Sleeper Performance
Bibliographic record
Abstract
The current research explores the load-bearing performance of geopolymer concrete sleepers as a sustainable alternative to conventional cement-based sleepers in railway infrastructure. Through rigorous comparative analysis, the study demonstrates that geopolymer sleepers exhibit comparable or superior strength characteristics under various loading conditions. The Key mechanical properties, including compressive strength, flexural strength, split tensile strength, and durability, are systematically assessed to evaluate the performance of geopolymer concrete thoroughly. The study investigates constant ratios of Ground Granulated Blast Furnace Slag (GGBS) to fly ash (60:40) and different ratios recycled coarse aggregate to natural coarse aggregate (100:0, 0:100, 70:30) to determine the optimal mix proportions. The experimental methodology employs a mix ratio of 1:1.28:3 and an 8 M alkaline solution with a NaOH to Na2SiO3 ratio of 1:2.5, alongside an alkaline solution to binder ratio of 0.43. Additionally, the static bending strength of the sleepers is analyzed, providing deeper insights into their structural performance. This research significantly advances the knowledge and application of geopolymer concrete in railway infrastructure, highlighting its potential for enhanced sustainability and durability.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".