Evaluation and Proposal of Strut-and-Tie Method for the Design of Drilled Shaft Footings
Bibliographic record
Abstract
This paper presents a practical, accurate, and reasonably conservative procedure for the design and analysis of drilled shaft footings, also referred to as pile caps. A database of drilled shaft footing tests was compiled from the literature to evaluate the accuracy of an existing design guide based on the three-dimensional (3D) strut-and-tie method (STM). It was concluded that strength estimations obtained with the existing 3D STM-based design guidelines were excessively conservative, and the accuracy of the method varied with key design parameters such as strut inclination and drilled shaft size. Key enhancements to the 3D STM are proposed to resolve existing limitations and ambiguities, including the definition of tie area for bottom mat reinforcement, 3D nodal geometry, nodal strength, concrete efficiency factor, and tie anchorage checks. These recommendations are supported by experimental evidence, including data from large-scale footing tests recently conducted by the authors, and are consistent with current design code provisions. The proposed method provides more accurate (less conservative) and less scattered (more reliable) strength estimations as compared to the existing recommendation. Lastly, a complete design example of a drilled shaft footing subjected to different loading scenarios is provided in the Supplemental Materials.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".