Soil Suffusion under the Dual Threat of Rainfall and Seismic Vibration
Bibliographic record
Abstract
This paper employs the systematic literature review (SLR) methodology to investigate the combined effects of seismic vibrations and rainfall on soil suffusion, a process leading to soil instability.Earthquake activity can accelerate soil liquefaction, exacerbating suffusion, while heavy rainfall can increase soil weight, inducing instability.Consequently, the repercussions of seismic activity and rainfall on suffusion may induce further damage and instability to civil infrastructure.The review reveals that the compound impact of rainfall and seismic vibrations can precipitate severe damage and instability, primarily through two mechanisms.First, earthquakes can catalyze soil liquefaction, inciting soil movement and amplifying the suffusion process.Second, heavy rainfall can saturate the soil, augmenting its weight and rendering it unstable, thereby inducing suffusion.However, the review also reveals a significant gap in understanding and mitigating suffusion triggered by simultaneous rainfall and seismic activity.Current techniques for identifying and mitigating such suffusion are inadequate, highlighting the need for further research.This review posits that the interaction of rainfall and seismic vibrations as a catalyst for soil suffusion demands additional scrutiny.It provides a comprehensive understanding of suffusion and the impact of rainfall and seismic vibrations on suffusive soils, serving as a basis for future studies on this important issue.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".