Contributions in Uncertainty Quantification Towards Reliability-based Rock Engineering Design
Bibliographic record
Abstract
This thesis contributes to two distinct but related problems in contemporary rock engineering design which arise in the context of developing limit states design (LSD) protocols. In doing so, it identifies the discrepancies between geotechnical limit states design standards and customary rock engineering design procedures. These discrepancies are shown to be classified as (i) difficulties regarding definition of limit state functions stemming from model uncertainty, and (ii) parameter uncertainties stemming from lack of sufficient quantitative data. With regards to (i) above, this thesis demonstrates the existence of a significant component of neglected and unquantifiable model uncertainty in rock engineering, embodied in the form of various subjective-qualitative schemes (e.g. RMR, Q and GSI) commonly used in rock engineering. We suggest that the term “nebulous models” better describes the role of such schemes in engineering design, and discuss how their common application is a major obstacle to achieving rock engineering LSD, overcoming which requires community-wide efforts and a fundamental change in mindset. This thesis continues to discuss (ii) above in more detail. We introduce Bayesian data analysis (BDA) which allows logical augmentation of data with information from other sources (i.e. relevant historical data and expert knowledge) as a potential solution to the problem of limited data in rock engineering. Limiting our investigation to analysis of intact rock strength data, we develop Bayesian regression models that accommodate variability of strength data, and characterize the to-date neglected associated strength parameter uncertainties. We particularly address simultaneous analysis of tensile and compressive strength data. Furthermore, we propose a hierarchical Bayesian model for meta-analysis of rock strength data which results in shrinkage of uncertainty of estimated parameters. We further discuss how the results of this model may be used to augment future data. Also, this thesis develops a quantile regression model for obtaining strength curves that comply with LSD standard definitions of characteristic values. Finally, Bayesian regression analysis is used to reveal and characterize a neglected statistical property of strength of strongly anisotropic rock, namely, unequal variance with respect to loading direction. This thesis concludes with suggestions for the research required to further develop rock engineering LSD.
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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 | 0.023 | 0.054 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".