2019 Canadian Geotechnical Colloquium: Mitigating a fatal flaw in modern geomechanics: understanding uncertainty, applying model calibration, and defying the hubris in numerical modelling
Bibliographic record
Abstract
This paper has been written to achieve two objectives. The first objective is to provide a discussion of the practical limitations of numerical modelling in the field of geomechanical engineering. Too many discussions of numerical methods in geomechanical engineering are centred on the impressive ability of numerical tools to conduct complex and sophisticated analyses with relative ease and efficiency. Practitioners need to have grounded conversations on numerical modelling regarding the reality that geomechanical designs are often data-limited with high degrees of uncertainty. When data limits and uncertainty are overlooked, geomechanical engineers are at risk of introducing unforeseen fatal flaws into engineering design. The second objective is to provide “how to” guidelines for model calibration using a variety of data types to qualify and quantify ground reaction. Model calibration is truly the only means to reduce numerical uncertainties. Formal training in numerical modelling is often focused on software utilization and computational methods; however, there are few opportunities for formal training on how to calibrate a model for practical engineering applications. This paper provides guidelines for calibration methods and procedures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".