A sequential method for selecting parameter values in the Barcelona basic model
Bibliographic record
Abstract
The popularity of the Barcelona basic model (BBM) has grown steadily since its publication in 1990, due to its ability to capture key aspects of unsaturated soil behaviour. Nevertheless, the BBM is still rarely employed by practitioners, partly because of the absence of simple and objective methods for selecting parameter values from laboratory tests. One difficulty is that, in the BBM, individual aspects of the isotropic virgin behaviour are controlled by multiple parameters, while at the same time a single parameter controls more than one aspect of soil behaviour. This has led to iterative procedures where parameter values are adjusted in turn to match experiments, which requires significant experience and can lead to the selection of widely varying parameter values depending on the user. The proposed method streamlines parameter selection with a view to increasing the appeal of the BBM for practitioners. The method adopts a “sequential” procedure where the five parameters governing isotropic virgin behaviour are matched to degrees of freedom in the BBM, which are then fixed one at a time, in a specific order, without any assumption about other parameters. The simplicity and reduced subjectivity of the method in comparison with iterative procedures is demonstrated by selecting parameter values from laboratory tests.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".