A Strut-and-Tie Model Approach to Design Precast Column-to-Pile Shaft Member Socket Connections against Prying-Action Failure
Bibliographic record
Abstract
This study presents a seismic design methodology for precast concrete column-to-pile shaft member socket connections. In designing such connections, the intent was to ensure that the plastic hinge occurs in columns while minimizing the damage to the pile shaft. To achieve this, a strut-and-tie model was developed to represent the force transfer mechanism within the connection region; this model served as the basis for efficient seismic design details, including shaft transverse reinforcement, column embedment length, and shaft size. The predictive accuracy of the strut-and-tie model was validated against both the experimental and finite-element analysis results for a wide variety of connection features. Upon validation, the strut-and-tie model was integrated into a step-by-step seismic design procedure and a case study was conducted to demonstrate the effectiveness of these procedures for determining suitable connection design details. Finally, a computer application with a graphical user interface was developed for the ease of implementation of the strut-and-tie model and reliable interpretation of its analysis results, making it possible to be used routinely in seismic design practice for precast column-to-pile shaft assemblies.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.006 | 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".