Consolidation analysis of inhomogeneous soil subjected to varied loading under impeded drainage based on the spectral method
Bibliographic record
Abstract
The consolidation behaviour of soft clays is significantly influenced by loading patterns, soil inhomogeneity, and boundary drainage conditions. Despite their significance, a notable gap exists in the availability of rigorous analytical solutions capable of integrating these factors into the consolidation analysis of inhomogeneous soft clay. This paper presents a comprehensive analysis of the consolidation behaviour of inhomogeneous soft clay subjected to impeded drainage conditions under time-dependent loading. The study employs the spectral method to derive solutions for specific distributions of inhomogeneous soil property distributions and loading patterns. The accuracy of the proposed method is validated through comparisons with previous analytical solutions, finite element method (FEM) solutions, laboratory tests and case studies, demonstrating improved predictive capabilities. A parametric study based on the proposed solution indicates that the dimensionless factors related to drainage capacity ( Rt and Rb) adversely impact soil consolidation, with smaller values leading to increased divergence from calculations assuming fully pervious conditions. Notably, solutions assuming fully pervious conditions can maintain considerable accuracy within a 5% error range only when Rt and Rb exceed a certain threshold (e.g., Rt > Rb/(0.049 Rb − 0.426) in this study). Otherwise, impeded drainage conditions must be considered for accurate consolidation calculations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".