Numerical Simulation of Displacement Functions of Strip Footings
Bibliographic record
Abstract
The risk associated with structures subjected to dynamic loading needs a rigorous analysis that takes into account dynamic soil-structure interaction. A key step of this analysis consists of estimating the dynamic response of the foundations by calculating their impedance (i.e. dynamic stiffness K and damping C) or displacement functions (real part F1 and imaginary part F2). The purpose of this work is to evaluate the displacement functions (inverse of impedance functions) of strip footings on the surface of some homogeneous soil. Validation studies indicate the accuracy and versatility of the models performed with the software FLAC (Fast Lagrangian Analysis of Continua). It is known that the assumption of homogenous layer or half space with constant shear modulus G may not be realistic as the shear wave velocity increases as a function of the effective overburden stress. In this paper, three soil models are considered. For each case, the adopted mechanical characteristics correspond to a type of soil with constant or variable shear wave velocity. Calculations are performed over a practically sufficient range of oscillating frequency ratios ao. Comparison of results obtained with varying and constant shear wave velocity shows the importance to consider this velocity increasing with depth. Additional calculations conducted on two-layer soil are also presented in order to recommend the thickness of soil that is required in the model to capture soil-structure interaction.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".