CANLEX full-scale experiment and modelling
Bibliographic record
Abstract
A major aim of the Canadian Liquefaction Experiment (CANLEX) was to verify analysis procedures for predicting liquefaction phenomena. Towards this purpose, two loading events were carried out: a field event comprising a clay embankment built over a loose sand foundation layer, and a centrifuge test performed on a model of a sand embankment structure. Both the field event and the centrifuge model were planned so as to induce a static liquefaction failure and were instrumented to observe their response in terms of displacement and pore pressure. The fundamental mechanical characteristics of the foundation layer were determined from laboratory element tests (triaxial and simple shear). These tests formed the basis for the stress-strain modelling used in the numerical analyses. Two fundamentally different modelling techniques were used. One involved a fully coupled plasticity model, and the other involved a model based on a collapse-surface approach. The model and prototype structures were then analyzed and the predicted results in terms of displacements and pore pressures were compared with the measured values. The results from both approaches were found to be in reasonable agreement with the measurements, provided allowance was made for direction of loading and drainage effects were accounted for.Key words: liquefaction, field experiment, embankment, centrifuge model, elastic-plastic model.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".