Enhancing the sustainability of slope stability in embankment construction by leveraging smart sensors and monitoring systems for data-driven insights
Bibliographic record
Abstract
Slopes are highly susceptible to instability during earthquakes, floods, and other natural disasters, often leading to landslides that pose serious risks to human life and property. This study investigates embankment slope stability through smart monitoring and reinforcement using geosynthetics. A novel polymer composite fertilizer (PCF) was characterized for its surface curing performance, resistance to temperature extremes, freeze-thaw aging, wind and water erosion, and its ability to neutralize soil acidity and alkalinity. The results demonstrate that PCF improves loess slope fixation and overall stability through both physical and chemical mechanisms. An early warning system is integrated into the framework to prevent significant property loss and fatalities. Principal Component Analysis (PCA) was employed to pre-process soil settlement data and identify outliers. A novel attention-constrained neural network optimized using the Marine Predator Algorithm (MPA) was proposed to extract key features from complex datasets for seismic slope stability prediction. Compared with ANFIS and EHO-NF models, the proposed model achieved a higher sensitivity of 0.788. Furthermore, the incorporation of biopolymers significantly enhanced resistance to shallow slope failures, offering a sustainable and adaptable solution for improving soil strength and long-term slope stability.
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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 | 0.001 |
| 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".