Shear capacity estimation of reinforced concrete deep beams using machine learning techniques
Bibliographic record
Abstract
Conventionally, the deep beam shear strength is analyzed with codes (mechanics and empirical models). The purpose of this investigation is to provide an alternative way of accurately estimating the shear capacity of Reinforced Concrete Deep Beams (RCDBs), including those with and without shear reinforcements (WOR and WWR), by adopting machine learning models. Four machine learning algorithms: k-Nearest Neighbor (kNN), Random Forest, M5Rules, and Sequential Minimal Optimization for Regression (SMOReg), were considered, and the selection was based on their performance in previous related studies. A database of 733 samples for WWR and 378 samples for WOR was compiled, utilizing 14 and 8 input features, respectively, in each case. WEKA, an open-source software suite, was used in preprocessing the data and also tuning the hyperparameters. SMOReg beat other models for WOR with an R² value of 0.9607, while Random Forest did best for WWR with an R² value of 0.9667 in the testing sets. The shear strengths predicted by the machine learning models were compared to four traditional standard codes. The results show that the machine learning models beat conventional methods by a large margin, while also being consistent with earlier models generated using machine learning. This demonstrates the model's prediction accuracy and robustness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".