Full-scale coupled torsion and lateral response of mast arm drilled shaft foundations
Bibliographic record
Abstract
Drilled shaft foundations supporting mast arm assemblies are subjected to significant torsion and lateral load during severe wind loading (e.g., hurricane). Past centrifuge studies in granular soils suggest that the design of such foundations should be performed for a coupled load case, since the lateral resistance is considerably reduced by the concurrent application of torsion. However, current design practice still considers lateral load and torsion independently due to the lack of field verification of centrifuge results. This paper reports on a full-scale test program to investigate the coupled load behavior of drilled shafts. A novel load test setup (with a heavy-duty mast arm assembly) and instrumentation were used for the combined torsion and lateral loading (e.g., wind loading). The study revealed a significant reduction in lateral resistance due to the influence of torque as observed from previous centrifuge studies. Torsional resistance was reduced (approximately 20%) by the impact of lateral load when compared with the anticipated torsional resistance based on unit skin friction values, derived from the axial load test results (i.e., no influence of lateral load). A comparison of measured torsional resistance during the combined loading with the predicted values using different approaches was also made. O’Neill and Hassan’s beta (β) method (sand) and alpha method (clay) are found to predict the torsional resistance reasonably well (±10%), while all other methods based on the standard penetration test and cone penetration test considered in the study overpredicted or underpredicted the resistance.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".