Direct Shear Strength Prediction for Precast Concrete Joints Using the Machine Learning Method
Why this work is in the frame
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Bibliographic record
Abstract
Precast segmental concrete beams (PSCBs) are being increasingly applied in bridges worldwide benefitting from the advantages of accelerated bridge construction. It is of importance to accurately predict the direct shear strength (DSS) of precast concrete joints (PCJs) for ensuring the safe structural design of PSCBs. However, existing prediction models of PCJs’ DSS are deemed inaccurate and unreliable when numerous parameters are varied in wide ranges. This study aims to establish an accurate and reliable prediction model for PCJs’ DSS using a machine learning algorithm called support vector regression (SVR). A PCJs’ DSS database of 304 test results with 23 input parameters was assembled from the literature. A model training procedure was conducted through stratified train-test split, feature scaling, feature selection, and two-step grid-search hyperparameter tuning. A new correlation matrix–based feature selection method was proposed, and three SVR models with different feature combinations were trained for validating the selection method. The trained SVR models were experimentally validated and compared with six existing mechanical models through two groups of performance indicators. A reasonable interpretation for the SVR model with the selected features in the proposed selection method was done using the combination of partial dependence (PD) and individual conditional expectation (ICE) plots. The results show that the SVR algorithm can be deemed feasible to accurately and reliably predict the DSS of PCJs. The proposed feature selection method is beneficial to the prediction performance of the SVR model. It is impossible for the typical mechanical models to achieve a similar prediction performance of the SVR model. The influence of each input parameter on the DSS of PCJs is recognized and depicted, which can offer useful information for further developing new mechanical models for predicting the DSS of PCJs with higher prediction performance in future research.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it