A Framework for Assessing Spatial Distribution of Soil Properties in Levee Systems Based on Underlying Geology and River Morphology
Bibliographic record
Abstract
Flood protection systems are complex, interconnected engineered systems, where failure at one location means the failure of the entire system. Earthen levees, the systems’ major component, are at risk from many causes of failure including seepage, overtopping, erosion and instability due to seismic loading. Levees stretch for long distances and are formed through various geologic processes and human activities over time, however information regarding soil properties is collected only at limited point locations and varies significantly both laterally and with depth. Prediction of levee performance in locations where no soil data is available becomes a limitation for system risk assessment studies. This study attempts to test the hypotheses that spatial variability of soil properties is correlated to regional variables such as distance from nearest river segment, river meandering sinuosity index and surface geology. A geostatistical ordinary kriging approach was used for developing these correlations. The specific areas used for data collection and analysis and model development in this study were sub-sections of the larger Sacramento River Flood Control Project (SRFCP) in northern California. Soil strength parameters of identified levee stratigraphy layers were statistically analyzed using a geostatistical ordinary kriging approach and correlated to preselected regional variables. Global observations that applied across the study area included the increasing trend of undrained shear strength for cohesive soils, Su, with increasing distance from the river, and decreasing trend of Su with increasing river Sinuosity Index levels. Only local trends were observed in the relation of friction angle of cohesionless soils, ?, with Sinuosity Index, as well as in the relation of Su and ? with geological formations.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".