Effectiveness of Subsurface Drainage for Mitigation of Landslides Affecting Pipelines
Bibliographic record
Abstract
Abstract Landslides can pose a threat to the integrity of new and existing pipelines if they are not mitigated. Improving subsurface drainage of groundwater is one of the most widely used stabilization strategies for mitigating landslides affecting pipelines because subsurface drainage requires minimal design and costs and can improve the overall stability. The appropriate design and implementation of this approach could lower the groundwater table within the landslide as a primary factor triggering landslide movement by reducing the driving force and increasing the shear strength or resisting force within the landslide mass. Thus, the subsurface drains are conventionally employed in the mitigation of most landslides that may threaten pipelines either as a single strategy or in conjunction with other measures. This paper presents how effective subsurface drainage systems are for improving slope stabilization in various site conditions. Included in the discussion is the predesign investigation considerations. The results of a series of two-dimensional limit equilibrium and seepage analyses are presented to evaluate the effectiveness of subsurface drainage systems. Site conditions explored in this paper include the location of the right-of-way compared to the boundary of landslide, geometry of landslide, and groundwater level. A model that uses genetic expression programming as a computational intelligence technique is introduced that predicts the effectiveness of subsurface drainage systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 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".