Calibration of constitutive parameters by inverse analysis for a geotechnical boundary problem
Bibliographic record
Abstract
The finite element method (FEM) has become a standard tool for solving complex problems in geotechnical engineering. Many different advanced constitutive models for fine-grained soils have been developed in recent years, which can consider various phenomena of soil. Generally, the number of constitutive parameters increases with phenomena incorporated in the model, and their determination is one of the key issues in numerical modeling in geotechnics. Normally, experimental data, experience, and back analyses based on engineering judgment are used to arrive at appropriate input parameters for a particular model. However, this procedure is not always satisfactory, especially when the number of required input parameters is large. In this paper, a population-based algorithm has been used to determine the constitutive parameters for a geotechnical boundary value problem, namely a floating stone column foundation under an embankment. Measurements of surface settlements and excess pore-water pressures at different depths are available for calibration. A subsequent statistical assessment of the calibration results is followed to assess the quality of the identified parameters in dependency of the different set of measurements evaluated. As a major result of this research, measures of the utility and reliability of the constitutive models for further predictive computations can be estimated.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".