Machine Learning Prediction of Headed Stud Shear Resistance in Profiled Corrugated Sheets for Steel-Concrete Composite Slabs
Bibliographic record
Abstract
Steel-concrete composite structures depend on headed shear studs for effective load transfer and composite action.Current design codes, such as EN 1994-1-1 and AISC 360-16, provide empirical formulas to estimate stud shear resistance.However, these codes often fail to account for failure mechanisms in modern profiled steel sheeting, resulting in unreliable predictions.This study evaluates the limitations of both codes using 611 push-out tests, revealing insufficient safety factors-1.09for EN provisions and 0.83 for AISC, both below the target of 1.25.To address these shortcomings, a machine learning-based approach was developed.Key preprocessing steps included outlier removal, feature scaling, and selection of critical features using XGBoost.Four models-XGBoost, LightGBM, Random Forest, and Decision Tree-were evaluated, with Random Forest achieving superior performance (R² = 0.9149, RMSE = 6.87,APE = 9.38%), outperforming traditional codes.A hybrid approach was devised by incorporating a safety factor of 1.25 into machine learning predictions.Adjusted predictions closely aligned with experimental results, yielding an average ratio of 1.24 and a robust R² of 0.93.Standard deviation comparisons highlighted a reduction of over 73% compared to EN provisions and 59% relative to AISC, ensuring improved reliability.The proposed methodology bridges the gap between empirical limitations and real-world behavior, providing a precise, data-driven tool for shear resistance estimation.By integrating machine learning, this approach enhances safety, precision, and applicability in structural design, addressing critical challenges in modern composite construction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".