A novel biopolymer-amended bentonite-based capillary barrier: performance evaluation against rainfall-induced landslides
Bibliographic record
Abstract
The growing demand for infrastructure in mountainous regions has increased landslide risks, highlighting the need for cost-effective and sustainable countermeasures. This study evaluates the performance of soft capillary barrier system (SCBS) using bentonite slurry (BS) enhanced with biopolymer (bentonite-XG slurry (BXGS)) compared to a field slope under natural drying-wetting cycles. Laboratory crack tests were conducted with 1%–5% biopolymer that identified 3% as optimal for crack reduction in BS for application in slopes. Hysteretic hydraulic properties of BXGS were evaluated to assess moisture dynamics, revealing a two-third reduction in saturated water content and a decrease in the order of 10−2 m/s in case of saturated hydraulic conductivity during wetting. The instrumented field prototypes were monitored over a year, and it was observed that the BXGS layer of SCBS reduced moisture infiltration by 30%–45% during wet seasons. A numerical model incorporating measured hysteretic hydraulic data, climate conditions, and infiltration-evaporation models using the 2D Richard's equation effectively validated field moisture variations. Subsequent seepage and stability analyses indicated that the SCBS implementation nearly doubled the factor of safety post-critical rainfall compared to the natural slope, highlighting its effectiveness in mitigating slope failures and enhancing infrastructure resilience in vulnerable regions.
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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) | 0.000 | 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 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".