Issue 75

SA. Farooq et alii, Fracture and Structural Integrity, 75 (2026) 362-372; DOI: 10.3221/IGF-ESIS.75.26

Figure 6: Predicted vs. actual fracture load plots for training and test data across six machine learning models trained on different combinations of experimental and synthetic data. The shaded region indicates a ±5% error band around the ideal prediction line. Performance of ML models for fracture load prediction The XGBoost models were trained to evaluate the effect of the synthetic data on the model accuracy and robustness in predicting the fracture loads of U-notched polycarbonate specimens. Fig. 6 presents the actual vs. predicted for all six different models with R 2 , MAPE, MAE, and RMSE, shown for each model. The model trained purely on 22 experimental

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