Instrumented load tests in mudstone: pile capacity and settlement prediction
Bibliographic record
Abstract
The present work stems from the design of a viaduct in South Wales, U.K., where full-scale pile testing was carried out to assess whether the proposed design methods would meet the required load capacity and settlement criteria for the working piles. Five fully instrumented large diameter bored cast in situ piles, up to 30 m deep, were installed in weathered mudstone and tested under vertical loading. A sixth pile, which had no shaft instrumentation, was formed with a voided toe. In conjunction with vast soil data from 218 site investigation boreholes, the extensive data produced from the load tests were analyzed to quantify the key parameters considered to influence load transfer and settlement behaviour. Each pile was first calibrated using four methods to establish the as-built stiffness, taking into account the nonlinearity of concrete and the effect of partial steel encasement. It is demonstrated that the current national norms for bored pile design in cohesive soil soft rock are overconservative for South Wales ground conditions. To ameliorate this, alternative methods are proposed, which lead to improved reliability and accuracy in shaft and base capacity assessment. In addition, a numerical model is developed that can be used to predict the complete load-settlement variation up to the ultimate state. The model is sufficiently expounded to allow its immediate application in pile design by geotechnical engineers.Key words: piled foundations, load tests, bearing capacity and settlement, Mercia mudstone.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".