Numerical modelling of rock anchor pullout and the influence of discrete fracture networks on the capacity of foundation tiedown anchors
Bibliographic record
Abstract
Numerous studies presented in this thesis have reported failure of the rock mass surrounding an anchor, as a result of applied external tensile loads (i.e. pullout loads) transferred to rock mass from the anchor and the overlying structure. Resistance to this failure mechanism is provided in design by assuming that the dead weight of a uniformly shaped inverted “cone”, with an assumed initiation point and breakout angle, provides resistance to the design loads. In some cases, a minor contribution of rock mass tensile or shear strength is considered by designers across the area of the assumed pullout cone. Strength estimates for this additional resistance are based primarily on sparse historic testing data, rock mass rating type relationships developed for other applications, and engineering judgement. However, rock mass rating systems assume that the rock mass is homogenous and isotropic, and at the scale of the anchor this assumption may not be valid since individual fractures may influence anchor stability. As an alternative to the current foundation anchor design method, this research presents a new approach to the rock cone pullout problem using Discrete Fracture Networks (DFN) combined with numerical simulations. The simulations presented in the research investigate the influence of fractures in a synthetic rock mass on ultimate anchor strength, with the purpose of developing a method for incorporation of scale effects of jointing in anchor design. By using numerical simulations that allow the load transfer mechanism from the anchor to the rock mass to vary with stiffness, it is contended that the failure mechanism of the rock mass under the applied loading can be considered more appropriately in anchor designs. It is also contended that some aleatory variability associated with fractures can be quantified using a DFN-based approach. Fractures are observed to have an influence on both the load distribution in the anchor as well as the ultimate resistance of the rock mass to pullout. The mapping considerations required to produce a DFN model for anchor pullout are described in this thesis and recommendations for incorporating DFN based models in anchor design are provided herein.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".