Experimental and Numerical Investigation of the Load-Bearing Mechanisms of Piles Socketed in Soft Rocks
Bibliographic record
Abstract
Abstract Rock-socket pile design predominantly depends on the shaft resistance to support the load at the serviceability state. However, due to limited understanding of the pile–rock interactions, the pile capacity is normally calculated using empirical correlations. In this study, the load-bearing mechanisms of rock-socketed piles were investigated through a miniaturised pile–load test setup in a soft synthetic rock. X-ray CT imaging and numerical discrete element modelling were used to investigate the micro-mechanics influencing the load-bearing mechanisms at the pile–rock interface. The numerical pile model was established based on suitable constitutive models capable of simulating the soft rock behaviour. The analysis of X-ray CT images at various displacements revealed three different interface mechanisms, namely sliding, local shearing and progressive shearing. The numerical model validated this observed micro-mechanics in the rock asperities through the evolution of damage and micro-cracks. Insights from the experimental and numerical results indicated that the height of the rock asperities significantly dictates the failure mode. Results also illustrated that the shaft load–displacement response primarily depends on the forces acting on the leading edges of the pile asperity. In particular, it was observed that the bottom leading edge carried a predominant portion of the shaft loads due to its connectivity with the rock at the base of the pile. Though negligible, the forces on the trailing edges provided valuable information on the contribution of residual shaft resistance by the debris at the interface. Moreover, the numerical studies revealed the different failure modes at the pile–rock interface. The discussions presented in this study provide novel insights into the load-bearing mechanisms of piles socketed in soft rocks, which will help to improve design guidelines in the future.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".