Soil spring stiffnesses for laterally loaded large diameter rigid caissons
Bibliographic record
Abstract
Laterally loaded large diameter caissons that exhibit rigid body motion are often analyzed by modeling the soil as equivalent springs. Equivalent soil spring stiffness is usually developed empirically. In this study, the soil spring stiffnesses are developed rigorously considering three-dimensional caisson-soil interaction based on a continuum-based analytical framework. A laterally loaded rigid circular caisson embedded in elastic soil is analyzed using the principle of virtual work. The soil displacement field is chosen based on the geometry and kinematics of the caisson, and the vertical displacement of the soil caused by the large-diameter caisson rotation is explicitly considered in the analysis, which has been neglected in previous studies. An iterative algorithm is used to obtain the solutions. It is shown that the caisson-soil interaction can be represented by a five-spring model along the shaft and at the base of the caisson. The equations of these spring stiffnesses are mathematically derived from the analysis without recourse to empiricism. Similarly, the equivalent spring stiffnesses of the caisson-soil system replacing the caisson and soil continuum are derived analytically from the solution. Based on the detailed parametric studies, fitted algebraic equations for these spring stiffnesses are obtained, which can be readily used by practicing engineers.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".