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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".