Estimation of slope annual failure probability considering uncertainty in groundwater table: a practical method based on fragility curve
Bibliographic record
Abstract
High uncertainties are often observed in groundwater conditions and soil properties in slope engineering practice, leading to difficulties in choosing a representative scenario of groundwater table (GWT) and representative soil properties for slope design. A wetting band approach is recommended by the Geotechnical Manual for Slopes in Hong Kong to determine a design GWT that corresponds to a 10-year return period rainfall. Using this design GWT, a deterministic slope stability analysis is performed to estimate a factor of safety ( FS) and evaluate slope safety. Although an FS value larger than 1 (e.g., 1.4) is often adopted in practice to lump the effect of all uncertainties by a single FS value, FS itself does not explicitly quantify the risk. To facilitate more effective assessment and mitigation of landslide hazards, various uncertainties shall be modeled explicitly and the occurrence of landslide hazards may be quantified by annual slope failure probability PFA. This study develops a fragility curve-based method for quantifying PFA with explicit modeling of uncertainties in groundwater conditions and soil properties. The proposed method is illustrated using a real slope in Hong Kong. The proposed method provides geotechnical engineers with a practical, reliability-based approach to slope design for landslide risk mitigation.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".