Predicting shear strength of hollow pretensioned spun precast concrete pile using machine learning models
Bibliographic record
Abstract
Pretensioned Spun Precast concrete (SPC) piles have become prevalent for deep foundation systems for their material quality, quick installation, and high uniformity along the length of the pile, which enhances both bearing capacity and overall structural performance. However, the reduced sectional area arising from the hollow core is a concern for SPC pile's overall shear capacity, particularly in the seismic zones . Conversely, concrete strength , high-strength strands and the application of prestressing improves the shear capacity of SPC piles. As a result, the involvement of more complex parameters makes the shear strength prediction complicated. This study explores the intricate domain of predictive modeling for the shear strength of SPC piles, employing twelve data-driven machine learning (ML) algorithms ranging from regression to boosting models. A comprehensive dataset consisting of 243 test results of SPC piles shear strength is meticulously assembled, structured, and employed to train and evaluate ML models. The input characteristics of the proposed ML models include the ratio of effective thickness to the outer diameter, the ratio of shear span to effective depth, the prestressing strand index, the non-prestressing strand index, the effective shear reinforcement index, the strength of the pile concrete, and the total effective axial stress . The SHapley Additive exPlanations (SHAP) analysis was conducted on the best-performing models to assess the influence of individual parameters on SPC piles shear strength and then compared to existing codes and provisions through sensitivity analysis. The outcome shows that XGB yielded the highest prediction accuracy and lowest variation, which outperformed the other mechanical models and code provisions. This study also demonstrates that the shear strength of SPC piles may be enhanced by raising the total effective axial stress, pile concrete strength, and the effective shear reinforcement index. In addition, a graphical user interface (GUI) has been established for the XGB model, which will aid practicing engineers and future studies in accurately forecasting the shear strength of SPC piles in construction applications.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".