Optimizing the fracture resistance of clay liners through fiber content and moisture control
Bibliographic record
Abstract
Abstract Structural integrity of clay liners in engineered waste landfills depends critically on their ability to resist the initiation and propagation of cracks under variable moisture conditions. In this study, the improvement of Mode I Fracture toughness KI in clayey soil through small additions of discrete glass fibers is investigated with particular emphasis on the interaction between fiber content and water content near the optimum moisture content (OMC). Specimens were prepared using a clayey soil compacted at water contents of 17%, 18%, and 19%, representing dry, optimum, and wet of optimum states based on proctor compaction test. Glass fibers were added uniformly at fractions of 0%, 0.01%, 0.02%, 0.05%, and 0.10% by weight of the soil. KI was obtained from single-edge notched beam (SENB) specimens tested in a three-point bending configuration. Load and displacement responses were analyzed to extract peak load Pmax and compute KI. The addition of only 0.01% glass fiber by mass enhances Pmax by 50%, resulting in a 70% increase in KI across all moisture conditions. These improvements are attributed to the effective interplay between clay particles bonded together with the glass fibers. The results also indicate that both Pmax and KI reach their maximum values near OMC (~ 18%), corresponding to the densest particle arrangement. However, increasing the fiber content beyond 0.01% leads to a decrease in KI and Pmax caused by fiber clustering, void formation, and weakened soil-fiber interfaces. The findings clearly illustrate that, by precisely limiting the water content and adding a sub-percent amount of glass fiber reinforcement, fracture resistance in clay liners increases significantly. This state-of-the-art approach offers a cost-effective and technically efficient strategy for enhancing the long-term performance of landfill systems to prevent seepage of harmful leachate to the groundwater.
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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".